Self-Adaptive Prediction of Cloud Resource Demands Using Ensemble Model and Subtractive-Fuzzy Clustering Based Fuzzy Neural Network

PubMed Central

Chen, Zhijia; Zhu, Yuanchang; Di, Yanqiang; Feng, Shaochong

2015-01-01

In IaaS (infrastructure as a service) cloud environment, users are provisioned with virtual machines (VMs). To allocate resources for users dynamically and effectively, accurate resource demands predicting is essential. For this purpose, this paper proposes a self-adaptive prediction method using ensemble model and subtractive-fuzzy clustering based fuzzy neural network (ESFCFNN). We analyze the characters of user preferences and demands. Then the architecture of the prediction model is constructed. We adopt some base predictors to compose the ensemble model. Then the structure and learning algorithm of fuzzy neural network is researched. To obtain the number of fuzzy rules and the initial value of the premise and consequent parameters, this paper proposes the fuzzy c-means combined with subtractive clustering algorithm, that is, the subtractive-fuzzy clustering. Finally, we adopt different criteria to evaluate the proposed method. The experiment results show that the method is accurate and effective in predicting the resource demands. PMID:25691896

Modeling Belt-Servomechanism by Chebyshev Functional Recurrent Neuro-Fuzzy Network

NASA Astrophysics Data System (ADS)

Huang, Yuan-Ruey; Kang, Yuan; Chu, Ming-Hui; Chang, Yeon-Pun

A novel Chebyshev functional recurrent neuro-fuzzy (CFRNF) network is developed from a combination of the Takagi-Sugeno-Kang (TSK) fuzzy model and the Chebyshev recurrent neural network (CRNN). The CFRNF network can emulate the nonlinear dynamics of a servomechanism system. The system nonlinearity is addressed by enhancing the input dimensions of the consequent parts in the fuzzy rules due to functional expansion of a Chebyshev polynomial. The back propagation algorithm is used to adjust the parameters of the antecedent membership functions as well as those of consequent functions. To verify the performance of the proposed CFRNF, the experiment of the belt servomechanism is presented in this paper. Both of identification methods of adaptive neural fuzzy inference system (ANFIS) and recurrent neural network (RNN) are also studied for modeling of the belt servomechanism. The analysis and comparison results indicate that CFRNF makes identification of complex nonlinear dynamic systems easier. It is verified that the accuracy and convergence of the CFRNF are superior to those of ANFIS and RNN by the identification results of a belt servomechanism.

Learning control of inverted pendulum system by neural network driven fuzzy reasoning: The learning function of NN-driven fuzzy reasoning under changes of reasoning environment

NASA Technical Reports Server (NTRS)

Hayashi, Isao; Nomura, Hiroyoshi; Wakami, Noboru

1991-01-01

Whereas conventional fuzzy reasonings are associated with tuning problems, which are lack of membership functions and inference rule designs, a neural network driven fuzzy reasoning (NDF) capable of determining membership functions by neural network is formulated. In the antecedent parts of the neural network driven fuzzy reasoning, the optimum membership function is determined by a neural network, while in the consequent parts, an amount of control for each rule is determined by other plural neural networks. By introducing an algorithm of neural network driven fuzzy reasoning, inference rules for making a pendulum stand up from its lowest suspended point are determined for verifying the usefulness of the algorithm.

Fuzzy neural network methodology applied to medical diagnosis

NASA Technical Reports Server (NTRS)

Gorzalczany, Marian B.; Deutsch-Mcleish, Mary

1992-01-01

This paper presents a technique for building expert systems that combines the fuzzy-set approach with artificial neural network structures. This technique can effectively deal with two types of medical knowledge: a nonfuzzy one and a fuzzy one which usually contributes to the process of medical diagnosis. Nonfuzzy numerical data is obtained from medical tests. Fuzzy linguistic rules describing the diagnosis process are provided by a human expert. The proposed method has been successfully applied in veterinary medicine as a support system in the diagnosis of canine liver diseases.

Optimizing Energy Consumption in Vehicular Sensor Networks by Clustering Using Fuzzy C-Means and Fuzzy Subtractive Algorithms

NASA Astrophysics Data System (ADS)

Ebrahimi, A.; Pahlavani, P.; Masoumi, Z.

2017-09-01

Traffic monitoring and managing in urban intelligent transportation systems (ITS) can be carried out based on vehicular sensor networks. In a vehicular sensor network, vehicles equipped with sensors such as GPS, can act as mobile sensors for sensing the urban traffic and sending the reports to a traffic monitoring center (TMC) for traffic estimation. The energy consumption by the sensor nodes is a main problem in the wireless sensor networks (WSNs); moreover, it is the most important feature in designing these networks. Clustering the sensor nodes is considered as an effective solution to reduce the energy consumption of WSNs. Each cluster should have a Cluster Head (CH), and a number of nodes located within its supervision area. The cluster heads are responsible for gathering and aggregating the information of clusters. Then, it transmits the information to the data collection center. Hence, the use of clustering decreases the volume of transmitting information, and, consequently, reduces the energy consumption of network. In this paper, Fuzzy C-Means (FCM) and Fuzzy Subtractive algorithms are employed to cluster sensors and investigate their performance on the energy consumption of sensors. It can be seen that the FCM algorithm and Fuzzy Subtractive have been reduced energy consumption of vehicle sensors up to 90.68% and 92.18%, respectively. Comparing the performance of the algorithms implies the 1.5 percent improvement in Fuzzy Subtractive algorithm in comparison.

Optimization of Close Range Photogrammetry Network Design Applying Fuzzy Computation

NASA Astrophysics Data System (ADS)

Aminia, A. S.

2017-09-01

Measuring object 3D coordinates with optimum accuracy is one of the most important issues in close range photogrammetry. In this context, network design plays an important role in determination of optimum position of imaging stations. This is, however, not a trivial task due to various geometric and radiometric constraints affecting the quality of the measurement network. As a result, most camera stations in the network are defined on a try and error basis based on the user's experience and generic network concept. In this paper, we propose a post-processing task to investigate the quality of camera positions right after image capturing to achieve the best result. To do this, a new fuzzy reasoning approach is adopted, in which the constraints affecting the network design are all modeled. As a result, the position of all camera locations is defined based on fuzzy rules and inappropriate stations are determined. The experiments carried out show that after determination and elimination of the inappropriate images using the proposed fuzzy reasoning system, the accuracy of measurements is improved and enhanced about 17% for the latter network.

Genetic Algorithm for Solving Fuzzy Shortest Path Problem in a Network with mixed fuzzy arc lengths

NASA Astrophysics Data System (ADS)

Mahdavi, Iraj; Tajdin, Ali; Hassanzadeh, Reza; Mahdavi-Amiri, Nezam; Shafieian, Hosna

2011-06-01

We are concerned with the design of a model and an algorithm for computing a shortest path in a network having various types of fuzzy arc lengths. First, we develop a new technique for the addition of various fuzzy numbers in a path using α -cuts by proposing a linear least squares model to obtain membership functions for the considered additions. Then, using a recently proposed distance function for comparison of fuzzy numbers. we propose a new approach to solve the fuzzy APSPP using of genetic algorithm. Examples are worked out to illustrate the applicability of the proposed model.

Wood texture classification by fuzzy neural networks

NASA Astrophysics Data System (ADS)

Gonzaga, Adilson; de Franca, Celso A.; Frere, Annie F.

1999-03-01

The majority of scientific papers focusing on wood classification for pencil manufacturing take into account defects and visual appearance. Traditional methodologies are base don texture analysis by co-occurrence matrix, by image modeling, or by tonal measures over the plate surface. In this work, we propose to classify plates of wood without biological defects like insect holes, nodes, and cracks, by analyzing their texture. By this methodology we divide the plate image in several rectangular windows or local areas and reduce the number of gray levels. From each local area, we compute the histogram of difference sand extract texture features, given them as input to a Local Neuro-Fuzzy Network. Those features are from the histogram of differences instead of the image pixels due to their better performance and illumination independence. Among several features like media, contrast, second moment, entropy, and IDN, the last three ones have showed better results for network training. Each LNN output is taken as input to a Partial Neuro-Fuzzy Network (PNFN) classifying a pencil region on the plate. At last, the outputs from the PNFN are taken as input to a Global Fuzzy Logic doing the plate classification. Each pencil classification within the plate is done taking into account each quality index.

Development of Energy Efficient Clustering Protocol in Wireless Sensor Network Using Neuro-Fuzzy Approach.

PubMed

Julie, E Golden; Selvi, S Tamil

2016-01-01

Wireless sensor networks (WSNs) consist of sensor nodes with limited processing capability and limited nonrechargeable battery power. Energy consumption in WSN is a significant issue in networks for improving network lifetime. It is essential to develop an energy aware clustering protocol in WSN to reduce energy consumption for increasing network lifetime. In this paper, a neuro-fuzzy energy aware clustering scheme (NFEACS) is proposed to form optimum and energy aware clusters. NFEACS consists of two parts: fuzzy subsystem and neural network system that achieved energy efficiency in forming clusters and cluster heads in WSN. NFEACS used neural network that provides effective training set related to energy and received signal strength of all nodes to estimate the expected energy for tentative cluster heads. Sensor nodes with higher energy are trained with center location of base station to select energy aware cluster heads. Fuzzy rule is used in fuzzy logic part that inputs to form clusters. NFEACS is designed for WSN handling mobility of node. The proposed scheme NFEACS is compared with related clustering schemes, cluster-head election mechanism using fuzzy logic, and energy aware fuzzy unequal clustering. The experiment results show that NFEACS performs better than the other related schemes.

Artificial Neural Networks Equivalent to Fuzzy Algebra T-Norm Conjunction Operators

NASA Astrophysics Data System (ADS)

Iliadis, L. S.; Spartalis, S. I.

2007-12-01

This paper describes the construction of three Artificial Neural Networks with fuzzy input and output, imitating the performance of fuzzy algebra conjunction operators. More specifically, it is applied over the results of a previous research effort that used T-Norms in order to produce a characteristic torrential risk index that unified the partial risk indices for the area of Xanthi. Each one of the three networks substitutes a T-Norm and consequently they can be used as equivalent operators. This means that ANN performing Fuzzy Algebra operations can be designed and developed.

Incomplete fuzzy data processing systems using artificial neural network

NASA Technical Reports Server (NTRS)

Patyra, Marek J.

1992-01-01

In this paper, the implementation of a fuzzy data processing system using an artificial neural network (ANN) is discussed. The binary representation of fuzzy data is assumed, where the universe of discourse is decartelized into n equal intervals. The value of a membership function is represented by a binary number. It is proposed that incomplete fuzzy data processing be performed in two stages. The first stage performs the 'retrieval' of incomplete fuzzy data, and the second stage performs the desired operation on the retrieval data. The method of incomplete fuzzy data retrieval is proposed based on the linear approximation of missing values of the membership function. The ANN implementation of the proposed system is presented. The system was computationally verified and showed a relatively small total error.

Fuzzy Neural Networks for Decision Support in Negotiation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sakas, D. P.; Vlachos, D. S.; Simos, T. E.

There is a large number of parameters which one can take into account when building a negotiation model. These parameters in general are uncertain, thus leading to models which represents them with fuzzy sets. On the other hand, the nature of these parameters makes them very difficult to model them with precise values. During negotiation, these parameters play an important role by altering the outcomes or changing the state of the negotiators. One reasonable way to model this procedure is to accept fuzzy relations (from theory or experience). The action of these relations to fuzzy sets, produce new fuzzy setsmore » which describe now the new state of the system or the modified parameters. But, in the majority of these situations, the relations are multidimensional, leading to complicated models and exponentially increasing computational time. In this paper a solution to this problem is presented. The use of fuzzy neural networks is shown that it can substitute the use of fuzzy relations with comparable results. Finally a simple simulation is carried in order to test the new method.« less

On the fusion of tuning parameters of fuzzy rules and neural network

NASA Astrophysics Data System (ADS)

Mamuda, Mamman; Sathasivam, Saratha

2017-08-01

Learning fuzzy rule-based system with neural network can lead to a precise valuable empathy of several problems. Fuzzy logic offers a simple way to reach at a definite conclusion based upon its vague, ambiguous, imprecise, noisy or missing input information. Conventional learning algorithm for tuning parameters of fuzzy rules using training input-output data usually end in a weak firing state, this certainly powers the fuzzy rule and makes it insecure for a multiple-input fuzzy system. In this paper, we introduce a new learning algorithm for tuning the parameters of the fuzzy rules alongside with radial basis function neural network (RBFNN) in training input-output data based on the gradient descent method. By the new learning algorithm, the problem of weak firing using the conventional method was addressed. We illustrated the efficiency of our new learning algorithm by means of numerical examples. MATLAB R2014(a) software was used in simulating our result The result shows that the new learning method has the best advantage of training the fuzzy rules without tempering with the fuzzy rule table which allowed a membership function of the rule to be used more than one time in the fuzzy rule base.

Logistics Distribution Center Location Evaluation Based on Genetic Algorithm and Fuzzy Neural Network

NASA Astrophysics Data System (ADS)

Shao, Yuxiang; Chen, Qing; Wei, Zhenhua

Logistics distribution center location evaluation is a dynamic, fuzzy, open and complicated nonlinear system, which makes it difficult to evaluate the distribution center location by the traditional analysis method. The paper proposes a distribution center location evaluation system which uses the fuzzy neural network combined with the genetic algorithm. In this model, the neural network is adopted to construct the fuzzy system. By using the genetic algorithm, the parameters of the neural network are optimized and trained so as to improve the fuzzy system’s abilities of self-study and self-adaptation. At last, the sampled data are trained and tested by Matlab software. The simulation results indicate that the proposed identification model has very small errors.

Development of Energy Efficient Clustering Protocol in Wireless Sensor Network Using Neuro-Fuzzy Approach

PubMed Central

Julie, E. Golden; Selvi, S. Tamil

2016-01-01

Wireless sensor networks (WSNs) consist of sensor nodes with limited processing capability and limited nonrechargeable battery power. Energy consumption in WSN is a significant issue in networks for improving network lifetime. It is essential to develop an energy aware clustering protocol in WSN to reduce energy consumption for increasing network lifetime. In this paper, a neuro-fuzzy energy aware clustering scheme (NFEACS) is proposed to form optimum and energy aware clusters. NFEACS consists of two parts: fuzzy subsystem and neural network system that achieved energy efficiency in forming clusters and cluster heads in WSN. NFEACS used neural network that provides effective training set related to energy and received signal strength of all nodes to estimate the expected energy for tentative cluster heads. Sensor nodes with higher energy are trained with center location of base station to select energy aware cluster heads. Fuzzy rule is used in fuzzy logic part that inputs to form clusters. NFEACS is designed for WSN handling mobility of node. The proposed scheme NFEACS is compared with related clustering schemes, cluster-head election mechanism using fuzzy logic, and energy aware fuzzy unequal clustering. The experiment results show that NFEACS performs better than the other related schemes. PMID:26881269

An architecture for designing fuzzy logic controllers using neural networks

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.

1991-01-01

Described here is an architecture for designing fuzzy controllers through a hierarchical process of control rule acquisition and by using special classes of neural network learning techniques. A new method for learning to refine a fuzzy logic controller is introduced. A reinforcement learning technique is used in conjunction with a multi-layer neural network model of a fuzzy controller. The model learns by updating its prediction of the plant's behavior and is related to the Sutton's Temporal Difference (TD) method. The method proposed here has the advantage of using the control knowledge of an experienced operator and fine-tuning it through the process of learning. The approach is applied to a cart-pole balancing system.

Recognition of Handwritten Arabic words using a neuro-fuzzy network

DOE Office of Scientific and Technical Information (OSTI.GOV)

Boukharouba, Abdelhak; Bennia, Abdelhak

We present a new method for the recognition of handwritten Arabic words based on neuro-fuzzy hybrid network. As a first step, connected components (CCs) of black pixels are detected. Then the system determines which CCs are sub-words and which are stress marks. The stress marks are then isolated and identified separately and the sub-words are segmented into graphemes. Each grapheme is described by topological and statistical features. Fuzzy rules are extracted from training examples by a hybrid learning scheme comprised of two phases: rule generation phase from data using a fuzzy c-means, and rule parameter tuning phase using gradient descentmore » learning. After learning, the network encodes in its topology the essential design parameters of a fuzzy inference system.The contribution of this technique is shown through the significant tests performed on a handwritten Arabic words database.« less

A fuzzy call admission control scheme in wireless networks

NASA Astrophysics Data System (ADS)

Ma, Yufeng; Gong, Shenguang; Hu, Xiulin; Zhang, Yunyu

2007-11-01

Scarcity of the spectrum resource and mobility of users make quality of service (QoS) provision a critical issue in wireless networks. This paper presents a fuzzy call admission control scheme to meet the requirement of the QoS. A performance measure is formed as a weighted linear function of new call and handoff call blocking probabilities. Simulation compares the proposed fuzzy scheme with an adaptive channel reservation scheme. Simulation results show that fuzzy scheme has a better robust performance in terms of average blocking criterion.

Proceedings of the Second Joint Technology Workshop on Neural Networks and Fuzzy Logic, volume 2

NASA Technical Reports Server (NTRS)

Lea, Robert N. (Editor); Villarreal, James A. (Editor)

1991-01-01

Documented here are papers presented at the Neural Networks and Fuzzy Logic Workshop sponsored by NASA and the University of Texas, Houston. Topics addressed included adaptive systems, learning algorithms, network architectures, vision, robotics, neurobiological connections, speech recognition and synthesis, fuzzy set theory and application, control and dynamics processing, space applications, fuzzy logic and neural network computers, approximate reasoning, and multiobject decision making.

Image segmentation using fuzzy LVQ clustering networks

NASA Technical Reports Server (NTRS)

Tsao, Eric Chen-Kuo; Bezdek, James C.; Pal, Nikhil R.

1992-01-01

In this note we formulate image segmentation as a clustering problem. Feature vectors extracted from a raw image are clustered into subregions, thereby segmenting the image. A fuzzy generalization of a Kohonen learning vector quantization (LVQ) which integrates the Fuzzy c-Means (FCM) model with the learning rate and updating strategies of the LVQ is used for this task. This network, which segments images in an unsupervised manner, is thus related to the FCM optimization problem. Numerical examples on photographic and magnetic resonance images are given to illustrate this approach to image segmentation.

Comments on "The multisynapse neural network and its application to fuzzy clustering".

PubMed

Yu, Jian; Hao, Pengwei

2005-05-01

In the above-mentioned paper, Wei and Fahn proposed a neural architecture, the multisynapse neural network, to solve constrained optimization problems including high-order, logarithmic, and sinusoidal forms, etc. As one of its main applications, a fuzzy bidirectional associative clustering network (FBACN) was proposed for fuzzy-partition clustering according to the objective-functional method. The connection between the objective-functional-based fuzzy c-partition algorithms and FBACN is the Lagrange multiplier approach. Unfortunately, the Lagrange multiplier approach was incorrectly applied so that FBACN does not equivalently minimize its corresponding constrained objective-function. Additionally, Wei and Fahn adopted traditional definition of fuzzy c-partition, which is not satisfied by FBACN. Therefore, FBACN can not solve constrained optimization problems, either.

Neural model of gene regulatory network: a survey on supportive meta-heuristics.

PubMed

Biswas, Surama; Acharyya, Sriyankar

2016-06-01

Gene regulatory network (GRN) is produced as a result of regulatory interactions between different genes through their coded proteins in cellular context. Having immense importance in disease detection and drug finding, GRN has been modelled through various mathematical and computational schemes and reported in survey articles. Neural and neuro-fuzzy models have been the focus of attraction in bioinformatics. Predominant use of meta-heuristic algorithms in training neural models has proved its excellence. Considering these facts, this paper is organized to survey neural modelling schemes of GRN and the efficacy of meta-heuristic algorithms towards parameter learning (i.e. weighting connections) within the model. This survey paper renders two different structure-related approaches to infer GRN which are global structure approach and substructure approach. It also describes two neural modelling schemes, such as artificial neural network/recurrent neural network based modelling and neuro-fuzzy modelling. The meta-heuristic algorithms applied so far to learn the structure and parameters of neutrally modelled GRN have been reviewed here.

Study on pattern recognition of Raman spectrum based on fuzzy neural network

NASA Astrophysics Data System (ADS)

Zheng, Xiangxiang; Lv, Xiaoyi; Mo, Jiaqing

2017-10-01

Hydatid disease is a serious parasitic disease in many regions worldwide, especially in Xinjiang, China. Raman spectrum of the serum of patients with echinococcosis was selected as the research object in this paper. The Raman spectrum of blood samples from healthy people and patients with echinococcosis are measured, of which the spectrum characteristics are analyzed. The fuzzy neural network not only has the ability of fuzzy logic to deal with uncertain information, but also has the ability to store knowledge of neural network, so it is combined with the Raman spectrum on the disease diagnosis problem based on Raman spectrum. Firstly, principal component analysis (PCA) is used to extract the principal components of the Raman spectrum, reducing the network input and accelerating the prediction speed and accuracy of Network based on remaining the original data. Then, the information of the extracted principal component is used as the input of the neural network, the hidden layer of the network is the generation of rules and the inference process, and the output layer of the network is fuzzy classification output. Finally, a part of samples are randomly selected for the use of training network, then the trained network is used for predicting the rest of the samples, and the predicted results are compared with general BP neural network to illustrate the feasibility and advantages of fuzzy neural network. Success in this endeavor would be helpful for the research work of spectroscopic diagnosis of disease and it can be applied in practice in many other spectral analysis technique fields.

Fuzzy knowledge base construction through belief networks based on Lukasiewicz logic

NASA Technical Reports Server (NTRS)

Lara-Rosano, Felipe

1992-01-01

In this paper, a procedure is proposed to build a fuzzy knowledge base founded on fuzzy belief networks and Lukasiewicz logic. Fuzzy procedures are developed to do the following: to assess the belief values of a consequent, in terms of the belief values of its logical antecedents and the belief value of the corresponding logical function; and to update belief values when new evidence is available.

Character recognition using a neural network model with fuzzy representation

NASA Technical Reports Server (NTRS)

Tavakoli, Nassrin; Seniw, David

1992-01-01

The degree to which digital images are recognized correctly by computerized algorithms is highly dependent upon the representation and the classification processes. Fuzzy techniques play an important role in both processes. In this paper, the role of fuzzy representation and classification on the recognition of digital characters is investigated. An experimental Neural Network model with application to character recognition was developed. Through a set of experiments, the effect of fuzzy representation on the recognition accuracy of this model is presented.

Proceedings of the Third International Workshop on Neural Networks and Fuzzy Logic, volume 2

NASA Technical Reports Server (NTRS)

Culbert, Christopher J. (Editor)

1993-01-01

Papers presented at the Neural Networks and Fuzzy Logic Workshop sponsored by the National Aeronautics and Space Administration and cosponsored by the University of Houston, Clear Lake, held 1-3 Jun. 1992 at the Lyndon B. Johnson Space Center in Houston, Texas are included. During the three days approximately 50 papers were presented. Technical topics addressed included adaptive systems; learning algorithms; network architectures; vision; robotics; neurobiological connections; speech recognition and synthesis; fuzzy set theory and application, control and dynamics processing; space applications; fuzzy logic and neural network computers; approximate reasoning; and multiobject decision making.

Proceedings of the Second Joint Technology Workshop on Neural Networks and Fuzzy Logic, volume 1

NASA Technical Reports Server (NTRS)

Lea, Robert N. (Editor); Villarreal, James (Editor)

1991-01-01

Documented here are papers presented at the Neural Networks and Fuzzy Logic Workshop sponsored by NASA and the University of Houston, Clear Lake. The workshop was held April 11 to 13 at the Johnson Space Flight Center. Technical topics addressed included adaptive systems, learning algorithms, network architectures, vision, robotics, neurobiological connections, speech recognition and synthesis, fuzzy set theory and application, control and dynamics processing, space applications, fuzzy logic and neural network computers, approximate reasoning, and multiobject decision making.

Hierarchical singleton-type recurrent neural fuzzy networks for noisy speech recognition.

PubMed

Juang, Chia-Feng; Chiou, Chyi-Tian; Lai, Chun-Lung

2007-05-01

This paper proposes noisy speech recognition using hierarchical singleton-type recurrent neural fuzzy networks (HSRNFNs). The proposed HSRNFN is a hierarchical connection of two singleton-type recurrent neural fuzzy networks (SRNFNs), where one is used for noise filtering and the other for recognition. The SRNFN is constructed by recurrent fuzzy if-then rules with fuzzy singletons in the consequences, and their recurrent properties make them suitable for processing speech patterns with temporal characteristics. In n words recognition, n SRNFNs are created for modeling n words, where each SRNFN receives the current frame feature and predicts the next one of its modeling word. The prediction error of each SRNFN is used as recognition criterion. In filtering, one SRNFN is created, and each SRNFN recognizer is connected to the same SRNFN filter, which filters noisy speech patterns in the feature domain before feeding them to the SRNFN recognizer. Experiments with Mandarin word recognition under different types of noise are performed. Other recognizers, including multilayer perceptron (MLP), time-delay neural networks (TDNNs), and hidden Markov models (HMMs), are also tested and compared. These experiments and comparisons demonstrate good results with HSRNFN for noisy speech recognition tasks.

Using Evolved Fuzzy Neural Networks for Injury Detection from Isokinetic Curves

NASA Astrophysics Data System (ADS)

Couchet, Jorge; Font, José María; Manrique, Daniel

In this paper we propose an evolutionary fuzzy neural networks system for extracting knowledge from a set of time series containing medical information. The series represent isokinetic curves obtained from a group of patients exercising the knee joint on an isokinetic dynamometer. The system has two parts: i) it analyses the time series input in order generate a simplified model of an isokinetic curve; ii) it applies a grammar-guided genetic program to obtain a knowledge base represented by a fuzzy neural network. Once the knowledge base has been generated, the system is able to perform knee injuries detection. The results suggest that evolved fuzzy neural networks perform better than non-evolutionary approaches and have a high accuracy rate during both the training and testing phases. Additionally, they are robust, as the system is able to self-adapt to changes in the problem without human intervention.

Proceedings of the Third International Workshop on Neural Networks and Fuzzy Logic, volume 1

NASA Technical Reports Server (NTRS)

Culbert, Christopher J. (Editor)

1993-01-01

Documented here are papers presented at the Neural Networks and Fuzzy Logic Workshop sponsored by the National Aeronautics and Space Administration and cosponsored by the University of Houston, Clear Lake. The workshop was held June 1-3, 1992 at the Lyndon B. Johnson Space Center in Houston, Texas. During the three days approximately 50 papers were presented. Technical topics addressed included adaptive systems; learning algorithms; network architectures; vision; robotics; neurobiological connections; speech recognition and synthesis; fuzzy set theory and application, control, and dynamics processing; space applications; fuzzy logic and neural network computers; approximate reasoning; and multiobject decision making.

Boundedness, Mittag-Leffler stability and asymptotical ω-periodicity of fractional-order fuzzy neural networks.

PubMed

Wu, Ailong; Zeng, Zhigang

2016-02-01

We show that the ω-periodic fractional-order fuzzy neural networks cannot generate non-constant ω-periodic signals. In addition, several sufficient conditions are obtained to ascertain the boundedness and global Mittag-Leffler stability of fractional-order fuzzy neural networks. Furthermore, S-asymptotical ω-periodicity and global asymptotical ω-periodicity of fractional-order fuzzy neural networks is also characterized. The obtained criteria improve and extend the existing related results. To illustrate and compare the theoretical criteria, some numerical examples with simulation results are discussed in detail. Crown Copyright © 2015. Published by Elsevier Ltd. All rights reserved.

Fuzzy Logic-Based Guaranteed Lifetime Protocol for Real-Time Wireless Sensor Networks.

PubMed

Shah, Babar; Iqbal, Farkhund; Abbas, Ali; Kim, Ki-Il

2015-08-18

Few techniques for guaranteeing a network lifetime have been proposed despite its great impact on network management. Moreover, since the existing schemes are mostly dependent on the combination of disparate parameters, they do not provide additional services, such as real-time communications and balanced energy consumption among sensor nodes; thus, the adaptability problems remain unresolved among nodes in wireless sensor networks (WSNs). To solve these problems, we propose a novel fuzzy logic model to provide real-time communication in a guaranteed WSN lifetime. The proposed fuzzy logic controller accepts the input descriptors energy, time and velocity to determine each node's role for the next duration and the next hop relay node for real-time packets. Through the simulation results, we verified that both the guaranteed network's lifetime and real-time delivery are efficiently ensured by the new fuzzy logic model. In more detail, the above-mentioned two performance metrics are improved up to 8%, as compared to our previous work, and 14% compared to existing schemes, respectively.

DFP: a Bioconductor package for fuzzy profile identification and gene reduction of microarray data

PubMed Central

Glez-Peña, Daniel; Álvarez, Rodrigo; Díaz, Fernando; Fdez-Riverola, Florentino

2009-01-01

Background Expression profiling assays done by using DNA microarray technology generate enormous data sets that are not amenable to simple analysis. The greatest challenge in maximizing the use of this huge amount of data is to develop algorithms to interpret and interconnect results from different genes under different conditions. In this context, fuzzy logic can provide a systematic and unbiased way to both (i) find biologically significant insights relating to meaningful genes, thereby removing the need for expert knowledge in preliminary steps of microarray data analyses and (ii) reduce the cost and complexity of later applied machine learning techniques being able to achieve interpretable models. Results DFP is a new Bioconductor R package that implements a method for discretizing and selecting differentially expressed genes based on the application of fuzzy logic. DFP takes advantage of fuzzy membership functions to assign linguistic labels to gene expression levels. The technique builds a reduced set of relevant genes (FP, Fuzzy Pattern) able to summarize and represent each underlying class (pathology). A last step constructs a biased set of genes (DFP, Discriminant Fuzzy Pattern) by intersecting existing fuzzy patterns in order to detect discriminative elements. In addition, the software provides new functions and visualisation tools that summarize achieved results and aid in the interpretation of differentially expressed genes from multiple microarray experiments. Conclusion DFP integrates with other packages of the Bioconductor project, uses common data structures and is accompanied by ample documentation. It has the advantage that its parameters are highly configurable, facilitating the discovery of biologically relevant connections between sets of genes belonging to different pathologies. This information makes it possible to automatically filter irrelevant genes thereby reducing the large volume of data supplied by microarray experiments. Based on

DFP: a Bioconductor package for fuzzy profile identification and gene reduction of microarray data.

PubMed

Glez-Peña, Daniel; Alvarez, Rodrigo; Díaz, Fernando; Fdez-Riverola, Florentino

2009-01-29

Expression profiling assays done by using DNA microarray technology generate enormous data sets that are not amenable to simple analysis. The greatest challenge in maximizing the use of this huge amount of data is to develop algorithms to interpret and interconnect results from different genes under different conditions. In this context, fuzzy logic can provide a systematic and unbiased way to both (i) find biologically significant insights relating to meaningful genes, thereby removing the need for expert knowledge in preliminary steps of microarray data analyses and (ii) reduce the cost and complexity of later applied machine learning techniques being able to achieve interpretable models. DFP is a new Bioconductor R package that implements a method for discretizing and selecting differentially expressed genes based on the application of fuzzy logic. DFP takes advantage of fuzzy membership functions to assign linguistic labels to gene expression levels. The technique builds a reduced set of relevant genes (FP, Fuzzy Pattern) able to summarize and represent each underlying class (pathology). A last step constructs a biased set of genes (DFP, Discriminant Fuzzy Pattern) by intersecting existing fuzzy patterns in order to detect discriminative elements. In addition, the software provides new functions and visualisation tools that summarize achieved results and aid in the interpretation of differentially expressed genes from multiple microarray experiments. DFP integrates with other packages of the Bioconductor project, uses common data structures and is accompanied by ample documentation. It has the advantage that its parameters are highly configurable, facilitating the discovery of biologically relevant connections between sets of genes belonging to different pathologies. This information makes it possible to automatically filter irrelevant genes thereby reducing the large volume of data supplied by microarray experiments. Based on these contributions GENECBR, a

An effective fuzzy kernel clustering analysis approach for gene expression data.

PubMed

Sun, Lin; Xu, Jiucheng; Yin, Jiaojiao

2015-01-01

Fuzzy clustering is an important tool for analyzing microarray data. A major problem in applying fuzzy clustering method to microarray gene expression data is the choice of parameters with cluster number and centers. This paper proposes a new approach to fuzzy kernel clustering analysis (FKCA) that identifies desired cluster number and obtains more steady results for gene expression data. First of all, to optimize characteristic differences and estimate optimal cluster number, Gaussian kernel function is introduced to improve spectrum analysis method (SAM). By combining subtractive clustering with max-min distance mean, maximum distance method (MDM) is proposed to determine cluster centers. Then, the corresponding steps of improved SAM (ISAM) and MDM are given respectively, whose superiority and stability are illustrated through performing experimental comparisons on gene expression data. Finally, by introducing ISAM and MDM into FKCA, an effective improved FKCA algorithm is proposed. Experimental results from public gene expression data and UCI database show that the proposed algorithms are feasible for cluster analysis, and the clustering accuracy is higher than the other related clustering algorithms.

Fuzzy Logic Based Anomaly Detection for Embedded Network Security Cyber Sensor

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ondrej Linda; Todd Vollmer; Jason Wright

Resiliency and security in critical infrastructure control systems in the modern world of cyber terrorism constitute a relevant concern. Developing a network security system specifically tailored to the requirements of such critical assets is of a primary importance. This paper proposes a novel learning algorithm for anomaly based network security cyber sensor together with its hardware implementation. The presented learning algorithm constructs a fuzzy logic rule based model of normal network behavior. Individual fuzzy rules are extracted directly from the stream of incoming packets using an online clustering algorithm. This learning algorithm was specifically developed to comply with the constrainedmore » computational requirements of low-cost embedded network security cyber sensors. The performance of the system was evaluated on a set of network data recorded from an experimental test-bed mimicking the environment of a critical infrastructure control system.« less

Robust stability for uncertain stochastic fuzzy BAM neural networks with time-varying delays

NASA Astrophysics Data System (ADS)

Syed Ali, M.; Balasubramaniam, P.

2008-07-01

In this Letter, by utilizing the Lyapunov functional and combining with the linear matrix inequality (LMI) approach, we analyze the global asymptotic stability of uncertain stochastic fuzzy Bidirectional Associative Memory (BAM) neural networks with time-varying delays which are represented by the Takagi-Sugeno (TS) fuzzy models. A new class of uncertain stochastic fuzzy BAM neural networks with time varying delays has been studied and sufficient conditions have been derived to obtain conservative result in stochastic settings. The developed results are more general than those reported in the earlier literatures. In addition, the numerical examples are provided to illustrate the applicability of the result using LMI toolbox in MATLAB.

New Models for Forecasting Enrollments: Fuzzy Time Series and Neural Network Approaches.

ERIC Educational Resources Information Center

Song, Qiang; Chissom, Brad S.

Since university enrollment forecasting is very important, many different methods and models have been proposed by researchers. Two new methods for enrollment forecasting are introduced: (1) the fuzzy time series model; and (2) the artificial neural networks model. Fuzzy time series has been proposed to deal with forecasting problems within a…

Nonlinear dynamic systems identification using recurrent interval type-2 TSK fuzzy neural network - A novel structure.

PubMed

El-Nagar, Ahmad M

2018-01-01

In this study, a novel structure of a recurrent interval type-2 Takagi-Sugeno-Kang (TSK) fuzzy neural network (FNN) is introduced for nonlinear dynamic and time-varying systems identification. It combines the type-2 fuzzy sets (T2FSs) and a recurrent FNN to avoid the data uncertainties. The fuzzy firing strengths in the proposed structure are returned to the network input as internal variables. The interval type-2 fuzzy sets (IT2FSs) is used to describe the antecedent part for each rule while the consequent part is a TSK-type, which is a linear function of the internal variables and the external inputs with interval weights. All the type-2 fuzzy rules for the proposed RIT2TSKFNN are learned on-line based on structure and parameter learning, which are performed using the type-2 fuzzy clustering. The antecedent and consequent parameters of the proposed RIT2TSKFNN are updated based on the Lyapunov function to achieve network stability. The obtained results indicate that our proposed network has a small root mean square error (RMSE) and a small integral of square error (ISE) with a small number of rules and a small computation time compared with other type-2 FNNs. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

A proposal of fuzzy connective with learning function and its application to fuzzy retrieval system

NASA Technical Reports Server (NTRS)

Hayashi, Isao; Naito, Eiichi; Ozawa, Jun; Wakami, Noboru

1993-01-01

A new fuzzy connective and a structure of network constructed by fuzzy connectives are proposed to overcome a drawback of conventional fuzzy retrieval systems. This network represents a retrieval query and the fuzzy connectives in networks have a learning function to adjust its parameters by data from a database and outputs of a user. The fuzzy retrieval systems employing this network are also constructed. Users can retrieve results even with a query whose attributes do not exist in a database schema and can get satisfactory results for variety of thinkings by learning function.

A clustering-based fuzzy wavelet neural network model for short-term load forecasting.

PubMed

Kodogiannis, Vassilis S; Amina, Mahdi; Petrounias, Ilias

2013-10-01

Load forecasting is a critical element of power system operation, involving prediction of the future level of demand to serve as the basis for supply and demand planning. This paper presents the development of a novel clustering-based fuzzy wavelet neural network (CB-FWNN) model and validates its prediction on the short-term electric load forecasting of the Power System of the Greek Island of Crete. The proposed model is obtained from the traditional Takagi-Sugeno-Kang fuzzy system by replacing the THEN part of fuzzy rules with a "multiplication" wavelet neural network (MWNN). Multidimensional Gaussian type of activation functions have been used in the IF part of the fuzzyrules. A Fuzzy Subtractive Clustering scheme is employed as a pre-processing technique to find out the initial set and adequate number of clusters and ultimately the number of multiplication nodes in MWNN, while Gaussian Mixture Models with the Expectation Maximization algorithm are utilized for the definition of the multidimensional Gaussians. The results corresponding to the minimum and maximum power load indicate that the proposed load forecasting model provides significantly accurate forecasts, compared to conventional neural networks models.

Prediction of soft soil foundation settlement in Guangxi granite area based on fuzzy neural network model

NASA Astrophysics Data System (ADS)

Luo, Junhui; Wu, Chao; Liu, Xianlin; Mi, Decai; Zeng, Fuquan; Zeng, Yongjun

2018-01-01

At present, the prediction of soft foundation settlement mostly use the exponential curve and hyperbola deferred approximation method, and the correlation between the results is poor. However, the application of neural network in this area has some limitations, and none of the models used in the existing cases adopted the TS fuzzy neural network of which calculation combines the characteristics of fuzzy system and neural network to realize the mutual compatibility methods. At the same time, the developed and optimized calculation program is convenient for engineering designers. Taking the prediction and analysis of soft foundation settlement of gully soft soil in granite area of Guangxi Guihe road as an example, the fuzzy neural network model is established and verified to explore the applicability. The TS fuzzy neural network is used to construct the prediction model of settlement and deformation, and the corresponding time response function is established to calculate and analyze the settlement of soft foundation. The results show that the prediction of short-term settlement of the model is accurate and the final settlement prediction result has certain engineering reference value.

Multi-modality image fusion based on enhanced fuzzy radial basis function neural networks.

PubMed

Chao, Zhen; Kim, Dohyeon; Kim, Hee-Joung

2018-04-01

In clinical applications, single modality images do not provide sufficient diagnostic information. Therefore, it is necessary to combine the advantages or complementarities of different modalities of images. Recently, neural network technique was applied to medical image fusion by many researchers, but there are still many deficiencies. In this study, we propose a novel fusion method to combine multi-modality medical images based on the enhanced fuzzy radial basis function neural network (Fuzzy-RBFNN), which includes five layers: input, fuzzy partition, front combination, inference, and output. Moreover, we propose a hybrid of the gravitational search algorithm (GSA) and error back propagation algorithm (EBPA) to train the network to update the parameters of the network. Two different patterns of images are used as inputs of the neural network, and the output is the fused image. A comparison with the conventional fusion methods and another neural network method through subjective observation and objective evaluation indexes reveals that the proposed method effectively synthesized the information of input images and achieved better results. Meanwhile, we also trained the network by using the EBPA and GSA, individually. The results reveal that the EBPGSA not only outperformed both EBPA and GSA, but also trained the neural network more accurately by analyzing the same evaluation indexes. Copyright © 2018 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

Fuzzy Logic-Based Guaranteed Lifetime Protocol for Real-Time Wireless Sensor Networks

PubMed Central

Shah, Babar; Iqbal, Farkhund; Abbas, Ali; Kim, Ki-Il

2015-01-01

Few techniques for guaranteeing a network lifetime have been proposed despite its great impact on network management. Moreover, since the existing schemes are mostly dependent on the combination of disparate parameters, they do not provide additional services, such as real-time communications and balanced energy consumption among sensor nodes; thus, the adaptability problems remain unresolved among nodes in wireless sensor networks (WSNs). To solve these problems, we propose a novel fuzzy logic model to provide real-time communication in a guaranteed WSN lifetime. The proposed fuzzy logic controller accepts the input descriptors energy, time and velocity to determine each node’s role for the next duration and the next hop relay node for real-time packets. Through the simulation results, we verified that both the guaranteed network’s lifetime and real-time delivery are efficiently ensured by the new fuzzy logic model. In more detail, the above-mentioned two performance metrics are improved up to 8%, as compared to our previous work, and 14% compared to existing schemes, respectively. PMID:26295238

Fuzzy mobile-robot positioning in intelligent spaces using wireless sensor networks.

PubMed

Herrero, David; Martínez, Humberto

2011-01-01

This work presents the development and experimental evaluation of a method based on fuzzy logic to locate mobile robots in an Intelligent Space using wireless sensor networks (WSNs). The problem consists of locating a mobile node using only inter-node range measurements, which are estimated by radio frequency signal strength attenuation. The sensor model of these measurements is very noisy and unreliable. The proposed method makes use of fuzzy logic for modeling and dealing with such uncertain information. Besides, the proposed approach is compared with a probabilistic technique showing that the fuzzy approach is able to handle highly uncertain situations that are difficult to manage by well-known localization methods.

An intelligent sales forecasting system through integration of artificial neural networks and fuzzy neural networks with fuzzy weight elimination.

PubMed

Kuo, R J; Wu, P; Wang, C P

2002-09-01

Sales forecasting plays a very prominent role in business strategy. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average (ARMA). However, sales forecasting is very complicated owing to influence by internal and external environments. Recently, artificial neural networks (ANNs) have also been applied in sales forecasting since their promising performances in the areas of control and pattern recognition. However, further improvement is still necessary since unique circumstances, e.g. promotion, cause a sudden change in the sales pattern. Thus, this study utilizes a proposed fuzzy neural network (FNN), which is able to eliminate the unimportant weights, for the sake of learning fuzzy IF-THEN rules obtained from the marketing experts with respect to promotion. The result from FNN is further integrated with the time series data through an ANN. Both the simulated and real-world problem results show that FNN with weight elimination can have lower training error compared with the regular FNN. Besides, real-world problem results also indicate that the proposed estimation system outperforms the conventional statistical method and single ANN in accuracy.

Training the Recurrent neural network by the Fuzzy Min-Max algorithm for fault prediction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zemouri, Ryad; Racoceanu, Daniel; Zerhouni, Noureddine

2009-03-05

In this paper, we present a training technique of a Recurrent Radial Basis Function neural network for fault prediction. We use the Fuzzy Min-Max technique to initialize the k-center of the RRBF neural network. The k-means algorithm is then applied to calculate the centers that minimize the mean square error of the prediction task. The performances of the k-means algorithm are then boosted by the Fuzzy Min-Max technique.

Study on application of adaptive fuzzy control and neural network in the automatic leveling system

NASA Astrophysics Data System (ADS)

Xu, Xiping; Zhao, Zizhao; Lan, Weiyong; Sha, Lei; Qian, Cheng

2015-04-01

This paper discusses the adaptive fuzzy control and neural network BP algorithm in large flat automatic leveling control system application. The purpose is to develop a measurement system with a flat quick leveling, Make the installation on the leveling system of measurement with tablet, to be able to achieve a level in precision measurement work quickly, improve the efficiency of the precision measurement. This paper focuses on the automatic leveling system analysis based on fuzzy controller, Use of the method of combining fuzzy controller and BP neural network, using BP algorithm improve the experience rules .Construct an adaptive fuzzy control system. Meanwhile the learning rate of the BP algorithm has also been run-rate adjusted to accelerate convergence. The simulation results show that the proposed control method can effectively improve the leveling precision of automatic leveling system and shorten the time of leveling.

Fuzzy and neural control

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.

1992-01-01

Fuzzy logic and neural networks provide new methods for designing control systems. Fuzzy logic controllers do not require a complete analytical model of a dynamic system and can provide knowledge-based heuristic controllers for ill-defined and complex systems. Neural networks can be used for learning control. In this chapter, we discuss hybrid methods using fuzzy logic and neural networks which can start with an approximate control knowledge base and refine it through reinforcement learning.

A Fuzzy analytical hierarchy process approach in irrigation networks maintenance

NASA Astrophysics Data System (ADS)

Riza Permana, Angga; Rintis Hadiani, Rr.; Syafi'i

2017-11-01

Ponorogo Regency has 440 Irrigation Area with a total area of 17,950 Ha. Due to the limited budget and lack of maintenance cause decreased function on the irrigation. The aim of this study is to make an appropriate system to determine the indices weighted of the rank prioritization criteria for irrigation network maintenance using a fuzzy-based methodology. The criteria that are used such as the physical condition of irrigation networks, area of service, estimated maintenance cost, and efficiency of irrigation water distribution. 26 experts in the field of water resources in the Dinas Pekerjaan Umum were asked to fill out the questionnaire, and the result will be used as a benchmark to determine the rank of irrigation network maintenance priority. The results demonstrate that the physical condition of irrigation networks criterion (W1) = 0,279 has the greatest impact on the assessment process. The area of service (W2) = 0,270, efficiency of irrigation water distribution (W4) = 0,249, and estimated maintenance cost (W3) = 0,202 criteria rank next in effectiveness, respectively. The proposed methodology deals with uncertainty and vague data using triangular fuzzy numbers, and, moreover, it provides a comprehensive decision-making technique to assess maintenance priority on irrigation network.

New Passivity Criteria for Fuzzy Bam Neural Networks with Markovian Jumping Parameters and Time-Varying Delays

NASA Astrophysics Data System (ADS)

Vadivel, P.; Sakthivel, R.; Mathiyalagan, K.; Thangaraj, P.

2013-02-01

This paper addresses the problem of passivity analysis issue for a class of fuzzy bidirectional associative memory (BAM) neural networks with Markovian jumping parameters and time varying delays. A set of sufficient conditions for the passiveness of the considered fuzzy BAM neural network model is derived in terms of linear matrix inequalities by using the delay fractioning technique together with the Lyapunov function approach. In addition, the uncertainties are inevitable in neural networks because of the existence of modeling errors and external disturbance. Further, this result is extended to study the robust passivity criteria for uncertain fuzzy BAM neural networks with time varying delays and uncertainties. These criteria are expressed in the form of linear matrix inequalities (LMIs), which can be efficiently solved via standard numerical software. Two numerical examples are provided to demonstrate the effectiveness of the obtained results.

The Satellite Clock Bias Prediction Method Based on Takagi-Sugeno Fuzzy Neural Network

NASA Astrophysics Data System (ADS)

Cai, C. L.; Yu, H. G.; Wei, Z. C.; Pan, J. D.

2017-05-01

The continuous improvement of the prediction accuracy of Satellite Clock Bias (SCB) is the key problem of precision navigation. In order to improve the precision of SCB prediction and better reflect the change characteristics of SCB, this paper proposes an SCB prediction method based on the Takagi-Sugeno fuzzy neural network. Firstly, the SCB values are pre-treated based on their characteristics. Then, an accurate Takagi-Sugeno fuzzy neural network model is established based on the preprocessed data to predict SCB. This paper uses the precise SCB data with different sampling intervals provided by IGS (International Global Navigation Satellite System Service) to realize the short-time prediction experiment, and the results are compared with the ARIMA (Auto-Regressive Integrated Moving Average) model, GM(1,1) model, and the quadratic polynomial model. The results show that the Takagi-Sugeno fuzzy neural network model is feasible and effective for the SCB short-time prediction experiment, and performs well for different types of clocks. The prediction results for the proposed method are better than the conventional methods obviously.

Design of double fuzzy clustering-driven context neural networks.

PubMed

Kim, Eun-Hu; Oh, Sung-Kwun; Pedrycz, Witold

2018-08-01

In this study, we introduce a novel category of double fuzzy clustering-driven context neural networks (DFCCNNs). The study is focused on the development of advanced design methodologies for redesigning the structure of conventional fuzzy clustering-based neural networks. The conventional fuzzy clustering-based neural networks typically focus on dividing the input space into several local spaces (implied by clusters). In contrast, the proposed DFCCNNs take into account two distinct local spaces called context and cluster spaces, respectively. Cluster space refers to the local space positioned in the input space whereas context space concerns a local space formed in the output space. Through partitioning the output space into several local spaces, each context space is used as the desired (target) local output to construct local models. To complete this, the proposed network includes a new context layer for reasoning about context space in the output space. In this sense, Fuzzy C-Means (FCM) clustering is useful to form local spaces in both input and output spaces. The first one is used in order to form clusters and train weights positioned between the input and hidden layer, whereas the other one is applied to the output space to form context spaces. The key features of the proposed DFCCNNs can be enumerated as follows: (i) the parameters between the input layer and hidden layer are built through FCM clustering. The connections (weights) are specified as constant terms being in fact the centers of the clusters. The membership functions (represented through the partition matrix) produced by the FCM are used as activation functions located at the hidden layer of the "conventional" neural networks. (ii) Following the hidden layer, a context layer is formed to approximate the context space of the output variable and each node in context layer means individual local model. The outputs of the context layer are specified as a combination of both weights formed as linear

Self-growing neural network architecture using crisp and fuzzy entropy

NASA Technical Reports Server (NTRS)

Cios, Krzysztof J.

1992-01-01

The paper briefly describes the self-growing neural network algorithm, CID2, which makes decision trees equivalent to hidden layers of a neural network. The algorithm generates a feedforward architecture using crisp and fuzzy entropy measures. The results of a real-life recognition problem of distinguishing defects in a glass ribbon and of a benchmark problem of differentiating two spirals are shown and discussed.

Self-growing neural network architecture using crisp and fuzzy entropy

NASA Technical Reports Server (NTRS)

Cios, Krzysztof J.

1992-01-01

The paper briefly describes the self-growing neural network algorithm, CID3, which makes decision trees equivalent to hidden layers of a neural network. The algorithm generates a feedforward architecture using crisp and fuzzy entropy measures. The results for a real-life recognition problem of distinguishing defects in a glass ribbon, and for a benchmark problen of telling two spirals apart are shown and discussed.

Fuzzy neural network for flow estimation in sewer systems during wet weather.

PubMed

Shen, Jun; Shen, Wei; Chang, Jian; Gong, Ning

2006-02-01

Estimation of the water flow from rainfall intensity during storm events is important in hydrology, sewer system control, and environmental protection. The runoff-producing behavior of a sewer system changes from one storm event to another because rainfall loss depends not only on rainfall intensities, but also on the state of the soil and vegetation, the general condition of the climate, and so on. As such, it would be difficult to obtain a precise flowrate estimation without sufficient a priori knowledge of these factors. To establish a model for flow estimation, one can also use statistical methods, such as the neural network STORMNET, software developed at Lyonnaise des Eaux, France, analyzing the relation between rainfall intensity and flowrate data of the known storm events registered in the past for a given sewer system. In this study, the authors propose a fuzzy neural network to estimate the flowrate from rainfall intensity. The fuzzy neural network combines four STORMNETs and fuzzy deduction to better estimate the flowrates. This study's system for flow estimation can be calibrated automatically by using known storm events; no data regarding the physical characteristics of the drainage basins are required. Compared with the neural network STORMNET, this method reduces the mean square error of the flow estimates by approximately 20%. Experimental results are reported herein.

A novel prosodic-information synthesizer based on recurrent fuzzy neural network for the Chinese TTS system.

PubMed

Lin, Chin-Teng; Wu, Rui-Cheng; Chang, Jyh-Yeong; Liang, Sheng-Fu

2004-02-01

In this paper, a new technique for the Chinese text-to-speech (TTS) system is proposed. Our major effort focuses on the prosodic information generation. New methodologies for constructing fuzzy rules in a prosodic model simulating human's pronouncing rules are developed. The proposed Recurrent Fuzzy Neural Network (RFNN) is a multilayer recurrent neural network (RNN) which integrates a Self-cOnstructing Neural Fuzzy Inference Network (SONFIN) into a recurrent connectionist structure. The RFNN can be functionally divided into two parts. The first part adopts the SONFIN as a prosodic model to explore the relationship between high-level linguistic features and prosodic information based on fuzzy inference rules. As compared to conventional neural networks, the SONFIN can always construct itself with an economic network size in high learning speed. The second part employs a five-layer network to generate all prosodic parameters by directly using the prosodic fuzzy rules inferred from the first part as well as other important features of syllables. The TTS system combined with the proposed method can behave not only sandhi rules but also the other prosodic phenomena existing in the traditional TTS systems. Moreover, the proposed scheme can even find out some new rules about prosodic phrase structure. The performance of the proposed RFNN-based prosodic model is verified by imbedding it into a Chinese TTS system with a Chinese monosyllable database based on the time-domain pitch synchronous overlap add (TD-PSOLA) method. Our experimental results show that the proposed RFNN can generate proper prosodic parameters including pitch means, pitch shapes, maximum energy levels, syllable duration, and pause duration. Some synthetic sounds are online available for demonstration.

Exponential stabilization and synchronization for fuzzy model of memristive neural networks by periodically intermittent control.

PubMed

Yang, Shiju; Li, Chuandong; Huang, Tingwen

2016-03-01

The problem of exponential stabilization and synchronization for fuzzy model of memristive neural networks (MNNs) is investigated by using periodically intermittent control in this paper. Based on the knowledge of memristor and recurrent neural network, the model of MNNs is formulated. Some novel and useful stabilization criteria and synchronization conditions are then derived by using the Lyapunov functional and differential inequality techniques. It is worth noting that the methods used in this paper are also applied to fuzzy model for complex networks and general neural networks. Numerical simulations are also provided to verify the effectiveness of theoretical results. Copyright © 2015 Elsevier Ltd. All rights reserved.

Application of Fuzzy-Logic Controller and Neural Networks Controller in Gas Turbine Speed Control and Overheating Control and Surge Control on Transient Performance

NASA Astrophysics Data System (ADS)

Torghabeh, A. A.; Tousi, A. M.

2007-08-01

This paper presents Fuzzy Logic and Neural Networks approach to Gas Turbine Fuel schedules. Modeling of non-linear system using feed forward artificial Neural Networks using data generated by a simulated gas turbine program is introduced. Two artificial Neural Networks are used , depicting the non-linear relationship between gas generator speed and fuel flow, and turbine inlet temperature and fuel flow respectively . Off-line fast simulations are used for engine controller design for turbojet engine based on repeated simulation. The Mamdani and Sugeno models are used to expression the Fuzzy system . The linguistic Fuzzy rules and membership functions are presents and a Fuzzy controller will be proposed to provide an Open-Loop control for the gas turbine engine during acceleration and deceleration . MATLAB Simulink was used to apply the Fuzzy Logic and Neural Networks analysis. Both systems were able to approximate functions characterizing the acceleration and deceleration schedules . Surge and Flame-out avoidance during acceleration and deceleration phases are then checked . Turbine Inlet Temperature also checked and controls by Neural Networks controller. This Fuzzy Logic and Neural Network Controllers output results are validated and evaluated by GSP software . The validation results are used to evaluate the generalization ability of these artificial Neural Networks and Fuzzy Logic controllers.

Fuzzy-Logic Based Distributed Energy-Efficient Clustering Algorithm for Wireless Sensor Networks.

PubMed

Zhang, Ying; Wang, Jun; Han, Dezhi; Wu, Huafeng; Zhou, Rundong

2017-07-03

Due to the high-energy efficiency and scalability, the clustering routing algorithm has been widely used in wireless sensor networks (WSNs). In order to gather information more efficiently, each sensor node transmits data to its Cluster Head (CH) to which it belongs, by multi-hop communication. However, the multi-hop communication in the cluster brings the problem of excessive energy consumption of the relay nodes which are closer to the CH. These nodes' energy will be consumed more quickly than the farther nodes, which brings the negative influence on load balance for the whole networks. Therefore, we propose an energy-efficient distributed clustering algorithm based on fuzzy approach with non-uniform distribution (EEDCF). During CHs' election, we take nodes' energies, nodes' degree and neighbor nodes' residual energies into consideration as the input parameters. In addition, we take advantage of Takagi, Sugeno and Kang (TSK) fuzzy model instead of traditional method as our inference system to guarantee the quantitative analysis more reasonable. In our scheme, each sensor node calculates the probability of being as CH with the help of fuzzy inference system in a distributed way. The experimental results indicate EEDCF algorithm is better than some current representative methods in aspects of data transmission, energy consumption and lifetime of networks.

Forecasting of natural gas consumption with neural network and neuro fuzzy system

NASA Astrophysics Data System (ADS)

Kaynar, Oguz; Yilmaz, Isik; Demirkoparan, Ferhan

2010-05-01

The prediction of natural gas consumption is crucial for Turkey which follows foreign-dependent policy in point of providing natural gas and whose stock capacity is only 5% of internal total consumption. Prediction accuracy of demand is one of the elements which has an influence on sectored investments and agreements about obtaining natural gas, so on development of sector. In recent years, new techniques, such as artificial neural networks and fuzzy inference systems, have been widely used in natural gas consumption prediction in addition to classical time series analysis. In this study, weekly natural gas consumption of Turkey has been predicted by means of three different approaches. The first one is Autoregressive Integrated Moving Average (ARIMA), which is classical time series analysis method. The second approach is the Artificial Neural Network. Two different ANN models, which are Multi Layer Perceptron (MLP) and Radial Basis Function Network (RBFN), are employed to predict natural gas consumption. The last is Adaptive Neuro Fuzzy Inference System (ANFIS), which combines ANN and Fuzzy Inference System. Different prediction models have been constructed and one model, which has the best forecasting performance, is determined for each method. Then predictions are made by using these models and results are compared. Keywords: ANN, ANFIS, ARIMA, Natural Gas, Forecasting

Fuzzy Modelling for Human Dynamics Based on Online Social Networks

PubMed Central

Cuenca-Jara, Jesus; Valdes-Vela, Mercedes; Skarmeta, Antonio F.

2017-01-01

Human mobility mining has attracted a lot of attention in the research community due to its multiple implications in the provisioning of innovative services for large metropolises. In this scope, Online Social Networks (OSN) have arisen as a promising source of location data to come up with new mobility models. However, the human nature of this data makes it rather noisy and inaccurate. In order to deal with such limitations, the present work introduces a framework for human mobility mining based on fuzzy logic. Firstly, a fuzzy clustering algorithm extracts the most active OSN areas at different time periods. Next, such clusters are the building blocks to compose mobility patterns. Furthermore, a location prediction service based on a fuzzy rule classifier has been developed on top of the framework. Finally, both the framework and the predictor has been tested with a Twitter and Flickr dataset in two large cities. PMID:28837120

Fuzzy Modelling for Human Dynamics Based on Online Social Networks.

PubMed

Cuenca-Jara, Jesus; Terroso-Saenz, Fernando; Valdes-Vela, Mercedes; Skarmeta, Antonio F

2017-08-24

Human mobility mining has attracted a lot of attention in the research community due to its multiple implications in the provisioning of innovative services for large metropolises. In this scope, Online Social Networks (OSN) have arisen as a promising source of location data to come up with new mobility models. However, the human nature of this data makes it rather noisy and inaccurate. In order to deal with such limitations, the present work introduces a framework for human mobility mining based on fuzzy logic. Firstly, a fuzzy clustering algorithm extracts the most active OSN areas at different time periods. Next, such clusters are the building blocks to compose mobility patterns. Furthermore, a location prediction service based on a fuzzy rule classifier has been developed on top of the framework. Finally, both the framework and the predictor has been tested with a Twitter and Flickr dataset in two large cities.

The Balanced Cross-Layer Design Routing Algorithm in Wireless Sensor Networks Using Fuzzy Logic.

PubMed

Li, Ning; Martínez, José-Fernán; Hernández Díaz, Vicente

2015-08-10

Recently, the cross-layer design for the wireless sensor network communication protocol has become more and more important and popular. Considering the disadvantages of the traditional cross-layer routing algorithms, in this paper we propose a new fuzzy logic-based routing algorithm, named the Balanced Cross-layer Fuzzy Logic (BCFL) routing algorithm. In BCFL, we use the cross-layer parameters' dispersion as the fuzzy logic inference system inputs. Moreover, we give each cross-layer parameter a dynamic weight according the value of the dispersion. For getting a balanced solution, the parameter whose dispersion is large will have small weight, and vice versa. In order to compare it with the traditional cross-layer routing algorithms, BCFL is evaluated through extensive simulations. The simulation results show that the new routing algorithm can handle the multiple constraints without increasing the complexity of the algorithm and can achieve the most balanced performance on selecting the next hop relay node. Moreover, the Balanced Cross-layer Fuzzy Logic routing algorithm can adapt to the dynamic changing of the network conditions and topology effectively.

The Balanced Cross-Layer Design Routing Algorithm in Wireless Sensor Networks Using Fuzzy Logic

PubMed Central

Li, Ning; Martínez, José-Fernán; Díaz, Vicente Hernández

2015-01-01

Recently, the cross-layer design for the wireless sensor network communication protocol has become more and more important and popular. Considering the disadvantages of the traditional cross-layer routing algorithms, in this paper we propose a new fuzzy logic-based routing algorithm, named the Balanced Cross-layer Fuzzy Logic (BCFL) routing algorithm. In BCFL, we use the cross-layer parameters’ dispersion as the fuzzy logic inference system inputs. Moreover, we give each cross-layer parameter a dynamic weight according the value of the dispersion. For getting a balanced solution, the parameter whose dispersion is large will have small weight, and vice versa. In order to compare it with the traditional cross-layer routing algorithms, BCFL is evaluated through extensive simulations. The simulation results show that the new routing algorithm can handle the multiple constraints without increasing the complexity of the algorithm and can achieve the most balanced performance on selecting the next hop relay node. Moreover, the Balanced Cross-layer Fuzzy Logic routing algorithm can adapt to the dynamic changing of the network conditions and topology effectively. PMID:26266412

Indirect adaptive fuzzy wavelet neural network with self- recurrent consequent part for AC servo system.

PubMed

Hou, Runmin; Wang, Li; Gao, Qiang; Hou, Yuanglong; Wang, Chao

2017-09-01

This paper proposes a novel indirect adaptive fuzzy wavelet neural network (IAFWNN) to control the nonlinearity, wide variations in loads, time-variation and uncertain disturbance of the ac servo system. In the proposed approach, the self-recurrent wavelet neural network (SRWNN) is employed to construct an adaptive self-recurrent consequent part for each fuzzy rule of TSK fuzzy model. For the IAFWNN controller, the online learning algorithm is based on back propagation (BP) algorithm. Moreover, an improved particle swarm optimization (IPSO) is used to adapt the learning rate. The aid of an adaptive SRWNN identifier offers the real-time gradient information to the adaptive fuzzy wavelet neural controller to overcome the impact of parameter variations, load disturbances and other uncertainties effectively, and has a good dynamic. The asymptotical stability of the system is guaranteed by using the Lyapunov method. The result of the simulation and the prototype test prove that the proposed are effective and suitable. Copyright © 2017. Published by Elsevier Ltd.

Design of hybrid radial basis function neural networks (HRBFNNs) realized with the aid of hybridization of fuzzy clustering method (FCM) and polynomial neural networks (PNNs).

PubMed

Huang, Wei; Oh, Sung-Kwun; Pedrycz, Witold

2014-12-01

In this study, we propose Hybrid Radial Basis Function Neural Networks (HRBFNNs) realized with the aid of fuzzy clustering method (Fuzzy C-Means, FCM) and polynomial neural networks. Fuzzy clustering used to form information granulation is employed to overcome a possible curse of dimensionality, while the polynomial neural network is utilized to build local models. Furthermore, genetic algorithm (GA) is exploited here to optimize the essential design parameters of the model (including fuzzification coefficient, the number of input polynomial fuzzy neurons (PFNs), and a collection of the specific subset of input PFNs) of the network. To reduce dimensionality of the input space, principal component analysis (PCA) is considered as a sound preprocessing vehicle. The performance of the HRBFNNs is quantified through a series of experiments, in which we use several modeling benchmarks of different levels of complexity (different number of input variables and the number of available data). A comparative analysis reveals that the proposed HRBFNNs exhibit higher accuracy in comparison to the accuracy produced by some models reported previously in the literature. Copyright © 2014 Elsevier Ltd. All rights reserved.

Hybrid algorithms for fuzzy reverse supply chain network design.

PubMed

Che, Z H; Chiang, Tzu-An; Kuo, Y C; Cui, Zhihua

2014-01-01

In consideration of capacity constraints, fuzzy defect ratio, and fuzzy transport loss ratio, this paper attempted to establish an optimized decision model for production planning and distribution of a multiphase, multiproduct reverse supply chain, which addresses defects returned to original manufacturers, and in addition, develops hybrid algorithms such as Particle Swarm Optimization-Genetic Algorithm (PSO-GA), Genetic Algorithm-Simulated Annealing (GA-SA), and Particle Swarm Optimization-Simulated Annealing (PSO-SA) for solving the optimized model. During a case study of a multi-phase, multi-product reverse supply chain network, this paper explained the suitability of the optimized decision model and the applicability of the algorithms. Finally, the hybrid algorithms showed excellent solving capability when compared with original GA and PSO methods.

Hybrid Algorithms for Fuzzy Reverse Supply Chain Network Design

PubMed Central

Che, Z. H.; Chiang, Tzu-An; Kuo, Y. C.

2014-01-01

In consideration of capacity constraints, fuzzy defect ratio, and fuzzy transport loss ratio, this paper attempted to establish an optimized decision model for production planning and distribution of a multiphase, multiproduct reverse supply chain, which addresses defects returned to original manufacturers, and in addition, develops hybrid algorithms such as Particle Swarm Optimization-Genetic Algorithm (PSO-GA), Genetic Algorithm-Simulated Annealing (GA-SA), and Particle Swarm Optimization-Simulated Annealing (PSO-SA) for solving the optimized model. During a case study of a multi-phase, multi-product reverse supply chain network, this paper explained the suitability of the optimized decision model and the applicability of the algorithms. Finally, the hybrid algorithms showed excellent solving capability when compared with original GA and PSO methods. PMID:24892057

Usefulness of Neuro-Fuzzy Models' Application for Tobacco Control

NASA Astrophysics Data System (ADS)

Petrovic-Lazarevic, Sonja; Zhang, Jian Ying

2007-12-01

The paper presents neuro-fuzzy models' application appropriate for tobacco control: the fuzzy control model, Adaptive Network Based Fuzzy Inference System, Evolving Fuzzy Neural Network models, and EVOlving POLicies. We propose further the use of Fuzzy Casual Networks to help tobacco control decision makers develop policies and measure their impact on social regulation.

Neural networks with fuzzy Petri nets for modeling a machining process

NASA Astrophysics Data System (ADS)

Hanna, Moheb M.

1998-03-01

The paper presents an intelligent architecture based a feedforward neural network with fuzzy Petri nets for modeling product quality in a CNC machining center. It discusses how the proposed architecture can be used for modeling, monitoring and control a product quality specification such as surface roughness. The surface roughness represents the output quality specification manufactured by a CNC machining center as a result of a milling process. The neural network approach employed the selected input parameters which defined by the machine operator via the CNC code. The fuzzy Petri nets approach utilized the exact input milling parameters, such as spindle speed, feed rate, tool diameter and coolant (off/on), which can be obtained via the machine or sensors system. An aim of the proposed architecture is to model the demanded quality of surface roughness as high, medium or low.

Fuzzy measures on the Gene Ontology for gene product similarity.

PubMed

Popescu, Mihail; Keller, James M; Mitchell, Joyce A

2006-01-01

One of the most important objects in bioinformatics is a gene product (protein or RNA). For many gene products, functional information is summarized in a set of Gene Ontology (GO) annotations. For these genes, it is reasonable to include similarity measures based on the terms found in the GO or other taxonomy. In this paper, we introduce several novel measures for computing the similarity of two gene products annotated with GO terms. The fuzzy measure similarity (FMS) has the advantage that it takes into consideration the context of both complete sets of annotation terms when computing the similarity between two gene products. When the two gene products are not annotated by common taxonomy terms, we propose a method that avoids a zero similarity result. To account for the variations in the annotation reliability, we propose a similarity measure based on the Choquet integral. These similarity measures provide extra tools for the biologist in search of functional information for gene products. The initial testing on a group of 194 sequences representing three proteins families shows a higher correlation of the FMS and Choquet similarities to the BLAST sequence similarities than the traditional similarity measures such as pairwise average or pairwise maximum.

A Fuzzy-Decision Based Approach for Composite Event Detection in Wireless Sensor Networks

PubMed Central

Zhang, Shukui; Chen, Hao; Zhu, Qiaoming

2014-01-01

The event detection is one of the fundamental researches in wireless sensor networks (WSNs). Due to the consideration of various properties that reflect events status, the Composite event is more consistent with the objective world. Thus, the research of the Composite event becomes more realistic. In this paper, we analyze the characteristics of the Composite event; then we propose a criterion to determine the area of the Composite event and put forward a dominating set based network topology construction algorithm under random deployment. For the unreliability of partial data in detection process and fuzziness of the event definitions in nature, we propose a cluster-based two-dimensional τ-GAS algorithm and fuzzy-decision based composite event decision mechanism. In the case that the sensory data of most nodes are normal, the two-dimensional τ-GAS algorithm can filter the fault node data effectively and reduce the influence of erroneous data on the event determination. The Composite event judgment mechanism which is based on fuzzy-decision holds the superiority of the fuzzy-logic based algorithm; moreover, it does not need the support of a huge rule base and its computational complexity is small. Compared to CollECT algorithm and CDS algorithm, this algorithm improves the detection accuracy and reduces the traffic. PMID:25136690

Fuzzy Counter Propagation Neural Network Control for a Class of Nonlinear Dynamical Systems

PubMed Central

Sakhre, Vandana; Jain, Sanjeev; Sapkal, Vilas S.; Agarwal, Dev P.

2015-01-01

Fuzzy Counter Propagation Neural Network (FCPN) controller design is developed, for a class of nonlinear dynamical systems. In this process, the weight connecting between the instar and outstar, that is, input-hidden and hidden-output layer, respectively, is adjusted by using Fuzzy Competitive Learning (FCL). FCL paradigm adopts the principle of learning, which is used to calculate Best Matched Node (BMN) which is proposed. This strategy offers a robust control of nonlinear dynamical systems. FCPN is compared with the existing network like Dynamic Network (DN) and Back Propagation Network (BPN) on the basis of Mean Absolute Error (MAE), Mean Square Error (MSE), Best Fit Rate (BFR), and so forth. It envisages that the proposed FCPN gives better results than DN and BPN. The effectiveness of the proposed FCPN algorithms is demonstrated through simulations of four nonlinear dynamical systems and multiple input and single output (MISO) and a single input and single output (SISO) gas furnace Box-Jenkins time series data. PMID:26366169

Fuzzy Counter Propagation Neural Network Control for a Class of Nonlinear Dynamical Systems.

PubMed

Sakhre, Vandana; Jain, Sanjeev; Sapkal, Vilas S; Agarwal, Dev P

2015-01-01

Fuzzy Counter Propagation Neural Network (FCPN) controller design is developed, for a class of nonlinear dynamical systems. In this process, the weight connecting between the instar and outstar, that is, input-hidden and hidden-output layer, respectively, is adjusted by using Fuzzy Competitive Learning (FCL). FCL paradigm adopts the principle of learning, which is used to calculate Best Matched Node (BMN) which is proposed. This strategy offers a robust control of nonlinear dynamical systems. FCPN is compared with the existing network like Dynamic Network (DN) and Back Propagation Network (BPN) on the basis of Mean Absolute Error (MAE), Mean Square Error (MSE), Best Fit Rate (BFR), and so forth. It envisages that the proposed FCPN gives better results than DN and BPN. The effectiveness of the proposed FCPN algorithms is demonstrated through simulations of four nonlinear dynamical systems and multiple input and single output (MISO) and a single input and single output (SISO) gas furnace Box-Jenkins time series data.

The 3-D image recognition based on fuzzy neural network technology

NASA Technical Reports Server (NTRS)

Hirota, Kaoru; Yamauchi, Kenichi; Murakami, Jun; Tanaka, Kei

1993-01-01

Three dimensional stereoscopic image recognition system based on fuzzy-neural network technology was developed. The system consists of three parts; preprocessing part, feature extraction part, and matching part. Two CCD color camera image are fed to the preprocessing part, where several operations including RGB-HSV transformation are done. A multi-layer perception is used for the line detection in the feature extraction part. Then fuzzy matching technique is introduced in the matching part. The system is realized on SUN spark station and special image input hardware system. An experimental result on bottle images is also presented.

A method of groundwater quality assessment based on fuzzy network-CANFIS and geographic information system (GIS)

NASA Astrophysics Data System (ADS)

Gholami, V.; Khaleghi, M. R.; Sebghati, M.

2017-11-01

The process of water quality testing is money/time-consuming, quite important and difficult stage for routine measurements. Therefore, use of models has become commonplace in simulating water quality. In this study, the coactive neuro-fuzzy inference system (CANFIS) was used to simulate groundwater quality. Further, geographic information system (GIS) was used as the pre-processor and post-processor tool to demonstrate spatial variation of groundwater quality. All important factors were quantified and groundwater quality index (GWQI) was developed. The proposed model was trained and validated by taking a case study of Mazandaran Plain located in northern part of Iran. The factors affecting groundwater quality were the input variables for the simulation, whereas GWQI index was the output. The developed model was validated to simulate groundwater quality. Network validation was performed via comparison between the estimated and actual GWQI values. In GIS, the study area was separated to raster format in the pixel dimensions of 1 km and also by incorporation of input data layers of the Fuzzy Network-CANFIS model; the geo-referenced layers of the effective factors in groundwater quality were earned. Therefore, numeric values of each pixel with geographical coordinates were entered to the Fuzzy Network-CANFIS model and thus simulation of groundwater quality was accessed in the study area. Finally, the simulated GWQI indices using the Fuzzy Network-CANFIS model were entered into GIS, and hence groundwater quality map (raster layer) based on the results of the network simulation was earned. The study's results confirm the high efficiency of incorporation of neuro-fuzzy techniques and GIS. It is also worth noting that the general quality of the groundwater in the most studied plain is fairly low.

Introduction to Fuzzy Set Theory

NASA Technical Reports Server (NTRS)

Kosko, Bart

1990-01-01

An introduction to fuzzy set theory is described. Topics covered include: neural networks and fuzzy systems; the dynamical systems approach to machine intelligence; intelligent behavior as adaptive model-free estimation; fuzziness versus probability; fuzzy sets; the entropy-subsethood theorem; adaptive fuzzy systems for backing up a truck-and-trailer; product-space clustering with differential competitive learning; and adaptive fuzzy system for target tracking.

A neural fuzzy controller learning by fuzzy error propagation

NASA Technical Reports Server (NTRS)

Nauck, Detlef; Kruse, Rudolf

1992-01-01

In this paper, we describe a procedure to integrate techniques for the adaptation of membership functions in a linguistic variable based fuzzy control environment by using neural network learning principles. This is an extension to our work. We solve this problem by defining a fuzzy error that is propagated back through the architecture of our fuzzy controller. According to this fuzzy error and the strength of its antecedent each fuzzy rule determines its amount of error. Depending on the current state of the controlled system and the control action derived from the conclusion, each rule tunes the membership functions of its antecedent and its conclusion. By this we get an unsupervised learning technique that enables a fuzzy controller to adapt to a control task by knowing just about the global state and the fuzzy error.

A Multi-level Fuzzy Evaluation Method for Smart Distribution Network Based on Entropy Weight

NASA Astrophysics Data System (ADS)

Li, Jianfang; Song, Xiaohui; Gao, Fei; Zhang, Yu

2017-05-01

Smart distribution network is considered as the future trend of distribution network. In order to comprehensive evaluate smart distribution construction level and give guidance to the practice of smart distribution construction, a multi-level fuzzy evaluation method based on entropy weight is proposed. Firstly, focus on both the conventional characteristics of distribution network and new characteristics of smart distribution network such as self-healing and interaction, a multi-level evaluation index system which contains power supply capability, power quality, economy, reliability and interaction is established. Then, a combination weighting method based on Delphi method and entropy weight method is put forward, which take into account not only the importance of the evaluation index in the experts’ subjective view, but also the objective and different information from the index values. Thirdly, a multi-level evaluation method based on fuzzy theory is put forward. Lastly, an example is conducted based on the statistical data of some cites’ distribution network and the evaluation method is proved effective and rational.

Fuzzy support vector machine: an efficient rule-based classification technique for microarrays.

PubMed

Hajiloo, Mohsen; Rabiee, Hamid R; Anooshahpour, Mahdi

2013-01-01

The abundance of gene expression microarray data has led to the development of machine learning algorithms applicable for tackling disease diagnosis, disease prognosis, and treatment selection problems. However, these algorithms often produce classifiers with weaknesses in terms of accuracy, robustness, and interpretability. This paper introduces fuzzy support vector machine which is a learning algorithm based on combination of fuzzy classifiers and kernel machines for microarray classification. Experimental results on public leukemia, prostate, and colon cancer datasets show that fuzzy support vector machine applied in combination with filter or wrapper feature selection methods develops a robust model with higher accuracy than the conventional microarray classification models such as support vector machine, artificial neural network, decision trees, k nearest neighbors, and diagonal linear discriminant analysis. Furthermore, the interpretable rule-base inferred from fuzzy support vector machine helps extracting biological knowledge from microarray data. Fuzzy support vector machine as a new classification model with high generalization power, robustness, and good interpretability seems to be a promising tool for gene expression microarray classification.

Towards Resilient Critical Infrastructures: Application of Type-2 Fuzzy Logic in Embedded Network Security Cyber Sensor

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ondrej Linda; Todd Vollmer; Jim Alves-Foss

2011-08-01

Resiliency and cyber security of modern critical infrastructures is becoming increasingly important with the growing number of threats in the cyber-environment. This paper proposes an extension to a previously developed fuzzy logic based anomaly detection network security cyber sensor via incorporating Type-2 Fuzzy Logic (T2 FL). In general, fuzzy logic provides a framework for system modeling in linguistic form capable of coping with imprecise and vague meanings of words. T2 FL is an extension of Type-1 FL which proved to be successful in modeling and minimizing the effects of various kinds of dynamic uncertainties. In this paper, T2 FL providesmore » a basis for robust anomaly detection and cyber security state awareness. In addition, the proposed algorithm was specifically developed to comply with the constrained computational requirements of low-cost embedded network security cyber sensors. The performance of the system was evaluated on a set of network data recorded from an experimental cyber-security test-bed.« less

A gene network simulator to assess reverse engineering algorithms.

PubMed

Di Camillo, Barbara; Toffolo, Gianna; Cobelli, Claudio

2009-03-01

In the context of reverse engineering of biological networks, simulators are helpful to test and compare the accuracy of different reverse-engineering approaches in a variety of experimental conditions. A novel gene-network simulator is presented that resembles some of the main features of transcriptional regulatory networks related to topology, interaction among regulators of transcription, and expression dynamics. The simulator generates network topology according to the current knowledge of biological network organization, including scale-free distribution of the connectivity and clustering coefficient independent of the number of nodes in the network. It uses fuzzy logic to represent interactions among the regulators of each gene, integrated with differential equations to generate continuous data, comparable to real data for variety and dynamic complexity. Finally, the simulator accounts for saturation in the response to regulation and transcription activation thresholds and shows robustness to perturbations. It therefore provides a reliable and versatile test bed for reverse engineering algorithms applied to microarray data. Since the simulator describes regulatory interactions and expression dynamics as two distinct, although interconnected aspects of regulation, it can also be used to test reverse engineering approaches that use both microarray and protein-protein interaction data in the process of learning. A first software release is available at http://www.dei.unipd.it/~dicamill/software/netsim as an R programming language package.

Application of a Fuzzy Neural Network Model in Predicting Polycyclic Aromatic Hydrocarbon- Mediated Perturbations of the Cyp1b1 Transcriptional Regulatory Network in Mouse Skin

PubMed Central

Larkin, Andrew; Siddens, Lisbeth K.; Krueger, Sharon K.; Tilton, Susan C.; Waters, Katrina M.; Williams, David E.; Baird, William M.

2013-01-01

Polycyclic aromatic hydrocarbons (PAHs) are present in the environment as complex mixtures with components that have diverse carcinogenic potencies and mostly unknown interactive effects. Non-additive PAH interactions have been observed in regulation of cytochrome P450 (CYP) gene expression in the CYP1 family. To better understand and predict biological effects of complex mixtures, such as environmental PAHs, an 11 gene input-1 gene output fuzzy neural network (FNN) was developed for predicting PAH-mediated perturbations of dermal Cyp1b1 transcription in mice. Input values were generalized using fuzzy logic into low, medium, and high fuzzy subsets, and sorted using k-means clustering to create Mamdani logic functions for predicting Cyp1b1 mRNA expression. Model testing was performed with data from microarray analysis of skin samples from FVB/N mice treated with toluene (vehicle control), dibenzo[def,p]chrysene (DBC), benzo[a]pyrene (BaP), or 1 of 3 combinations of diesel particulate extract (DPE), coal tar extract (CTE) and cigarette smoke condensate (CSC) using leave one out cross-validation. Predictions were within 1 log2 fold change unit of microarray data, with the exception of the DBC treatment group, where the unexpected down-regulation of Cyp1b1 expression was predicted but did not reach statistical significance on the microarrays. Adding CTE to DPE was predicted to increase Cyp1b1 expression, whereas adding CSC to CTE and DPE was predicted to have no effect, in agreement with microarray results. The aryl hydrocarbon receptor repressor (Ahrr) was determined to be the most significant input variable for model predictions using back-propagation and normalization of FNN weights. PMID:23274566

Fuzzy Edge Connectivity of Graphical Fuzzy State Space Model in Multi-connected System

NASA Astrophysics Data System (ADS)

Harish, Noor Ainy; Ismail, Razidah; Ahmad, Tahir

2010-11-01

Structured networks of interacting components illustrate complex structure in a direct or intuitive way. Graph theory provides a mathematical modeling for studying interconnection among elements in natural and man-made systems. On the other hand, directed graph is useful to define and interpret the interconnection structure underlying the dynamics of the interacting subsystem. Fuzzy theory provides important tools in dealing various aspects of complexity, imprecision and fuzziness of the network structure of a multi-connected system. Initial development for systems of Fuzzy State Space Model (FSSM) and a fuzzy algorithm approach were introduced with the purpose of solving the inverse problems in multivariable system. In this paper, fuzzy algorithm is adapted in order to determine the fuzzy edge connectivity between subsystems, in particular interconnected system of Graphical Representation of FSSM. This new approach will simplify the schematic diagram of interconnection of subsystems in a multi-connected system.

Observer-Based Non-PDC Control for Networked T-S Fuzzy Systems With an Event-Triggered Communication.

PubMed

Peng, Chen; Ma, Shaodong; Xie, Xiangpeng

2017-02-07

This paper addresses the problem of an event-triggered non-parallel distribution compensation (PDC) control for networked Takagi-Sugeno (T-S) fuzzy systems, under consideration of the limited data transmission bandwidth and the imperfect premise matching membership functions. First, a unified event-triggered T-S fuzzy model is provided, in which: 1) a fuzzy observer with the imperfect premise matching is constructed to estimate the unmeasurable states of the studied system; 2) a fuzzy controller is designed following the same premise as the observer; and 3) an output-based event-triggering transmission scheme is designed to economize the restricted network resources. Different from the traditional PDC method, the synchronous premise between the fuzzy observer and the T-S fuzzy system are no longer needed in this paper. Second, by use of Lyapunov theory, a stability criterion and a stabilization condition are obtained for ensuring asymptotically stable of the studied system. On account of the imperfect premise matching conditions are well considered in the derivation of the above criteria, less conservation can be expected to enhance the design flexibility. Compared with some existing emulation-based methods, the controller gains are no longer required to be known a priori. Finally, the availability of proposed non-PDC design scheme is illustrated by the backing-up control of a truck-trailer system.

Real-time flood forecasts & risk assessment using a possibility-theory based fuzzy neural network

NASA Astrophysics Data System (ADS)

Khan, U. T.

2016-12-01

Globally floods are one of the most devastating natural disasters and improved flood forecasting methods are essential for better flood protection in urban areas. Given the availability of high resolution real-time datasets for flood variables (e.g. streamflow and precipitation) in many urban areas, data-driven models have been effectively used to predict peak flow rates in river; however, the selection of input parameters for these types of models is often subjective. Additionally, the inherit uncertainty associated with data models along with errors in extreme event observations means that uncertainty quantification is essential. Addressing these concerns will enable improved flood forecasting methods and provide more accurate flood risk assessments. In this research, a new type of data-driven model, a quasi-real-time updating fuzzy neural network is developed to predict peak flow rates in urban riverine watersheds. A possibility-to-probability transformation is first used to convert observed data into fuzzy numbers. A possibility theory based training regime is them used to construct the fuzzy parameters and the outputs. A new entropy-based optimisation criterion is used to train the network. Two existing methods to select the optimum input parameters are modified to account for fuzzy number inputs, and compared. These methods are: Entropy-Wavelet-based Artificial Neural Network (EWANN) and Combined Neural Pathway Strength Analysis (CNPSA). Finally, an automated algorithm design to select the optimum structure of the neural network is implemented. The overall impact of each component of training this network is to replace the traditional ad hoc network configuration methods, with one based on objective criteria. Ten years of data from the Bow River in Calgary, Canada (including two major floods in 2005 and 2013) are used to calibrate and test the network. The EWANN method selected lagged peak flow as a candidate input, whereas the CNPSA method selected lagged

A software sensor model based on hybrid fuzzy neural network for rapid estimation water quality in Guangzhou section of Pearl River, China.

PubMed

Zhou, Chunshan; Zhang, Chao; Tian, Di; Wang, Ke; Huang, Mingzhi; Liu, Yanbiao

2018-01-02

In order to manage water resources, a software sensor model was designed to estimate water quality using a hybrid fuzzy neural network (FNN) in Guangzhou section of Pearl River, China. The software sensor system was composed of data storage module, fuzzy decision-making module, neural network module and fuzzy reasoning generator module. Fuzzy subtractive clustering was employed to capture the character of model, and optimize network architecture for enhancing network performance. The results indicate that, on basis of available on-line measured variables, the software sensor model can accurately predict water quality according to the relationship between chemical oxygen demand (COD) and dissolved oxygen (DO), pH and NH 4 + -N. Owing to its ability in recognizing time series patterns and non-linear characteristics, the software sensor-based FNN is obviously superior to the traditional neural network model, and its R (correlation coefficient), MAPE (mean absolute percentage error) and RMSE (root mean square error) are 0.8931, 10.9051 and 0.4634, respectively.

Fuzzy Logic Module of Convolutional Neural Network for Handwritten Digits Recognition

NASA Astrophysics Data System (ADS)

Popko, E. A.; Weinstein, I. A.

2016-08-01

Optical character recognition is one of the important issues in the field of pattern recognition. This paper presents a method for recognizing handwritten digits based on the modeling of convolutional neural network. The integrated fuzzy logic module based on a structural approach was developed. Used system architecture adjusted the output of the neural network to improve quality of symbol identification. It was shown that proposed algorithm was flexible and high recognition rate of 99.23% was achieved.

Backstepping fuzzy-neural-network control design for hybrid maglev transportation system.

PubMed

Wai, Rong-Jong; Yao, Jing-Xiang; Lee, Jeng-Dao

2015-02-01

This paper focuses on the design of a backstepping fuzzy-neural-network control (BFNNC) for the online levitated balancing and propulsive positioning of a hybrid magnetic levitation (maglev) transportation system. The dynamic model of the hybrid maglev transportation system including levitated hybrid electromagnets to reduce the suspension power loss and the friction force during linear movement and a propulsive linear induction motor based on the concepts of mechanical geometry and motion dynamics is first constructed. The ultimate goal is to design an online fuzzy neural network (FNN) control methodology to cope with the problem of the complicated control transformation and the chattering control effort in backstepping control (BSC) design, and to directly ensure the stability of the controlled system without the requirement of strict constraints, detailed system information, and auxiliary compensated controllers despite the existence of uncertainties. In the proposed BFNNC scheme, an FNN control is utilized to be the major control role by imitating the BSC strategy, and adaptation laws for network parameters are derived in the sense of projection algorithm and Lyapunov stability theorem to ensure the network convergence as well as stable control performance. The effectiveness of the proposed control strategy for the hybrid maglev transportation system is verified by experimental results, and the superiority of the BFNNC scheme is indicated in comparison with the BSC strategy and the backstepping particle-swarm-optimization control system in previous research.

a Modified Genetic Algorithm for Finding Fuzzy Shortest Paths in Uncertain Networks

NASA Astrophysics Data System (ADS)

Heidari, A. A.; Delavar, M. R.

2016-06-01

In realistic network analysis, there are several uncertainties in the measurements and computation of the arcs and vertices. These uncertainties should also be considered in realizing the shortest path problem (SPP) due to the inherent fuzziness in the body of expert's knowledge. In this paper, we investigated the SPP under uncertainty to evaluate our modified genetic strategy. We improved the performance of genetic algorithm (GA) to investigate a class of shortest path problems on networks with vague arc weights. The solutions of the uncertain SPP with considering fuzzy path lengths are examined and compared in detail. As a robust metaheuristic, GA algorithm is modified and evaluated to tackle the fuzzy SPP (FSPP) with uncertain arcs. For this purpose, first, a dynamic operation is implemented to enrich the exploration/exploitation patterns of the conventional procedure and mitigate the premature convergence of GA technique. Then, the modified GA (MGA) strategy is used to resolve the FSPP. The attained results of the proposed strategy are compared to those of GA with regard to the cost, quality of paths and CPU times. Numerical instances are provided to demonstrate the success of the proposed MGA-FSPP strategy in comparison with GA. The simulations affirm that not only the proposed technique can outperform GA, but also the qualities of the paths are effectively improved. The results clarify that the competence of the proposed GA is preferred in view of quality quantities. The results also demonstrate that the proposed method can efficiently be utilized to handle FSPP in uncertain networks.

Fuzzy-rule-based Adaptive Resource Control for Information Sharing in P2P Networks

NASA Astrophysics Data System (ADS)

Wu, Zhengping; Wu, Hao

With more and more peer-to-peer (P2P) technologies available for online collaboration and information sharing, people can launch more and more collaborative work in online social networks with friends, colleagues, and even strangers. Without face-to-face interactions, the question of who can be trusted and then share information with becomes a big concern of a user in these online social networks. This paper introduces an adaptive control service using fuzzy logic in preference definition for P2P information sharing control, and designs a novel decision-making mechanism using formal fuzzy rules and reasoning mechanisms adjusting P2P information sharing status following individual users' preferences. Applications of this adaptive control service into different information sharing environments show that this service can provide a convenient and accurate P2P information sharing control for individual users in P2P networks.

Hierarchical Gene Selection and Genetic Fuzzy System for Cancer Microarray Data Classification

PubMed Central

Nguyen, Thanh; Khosravi, Abbas; Creighton, Douglas; Nahavandi, Saeid

2015-01-01

This paper introduces a novel approach to gene selection based on a substantial modification of analytic hierarchy process (AHP). The modified AHP systematically integrates outcomes of individual filter methods to select the most informative genes for microarray classification. Five individual ranking methods including t-test, entropy, receiver operating characteristic (ROC) curve, Wilcoxon and signal to noise ratio are employed to rank genes. These ranked genes are then considered as inputs for the modified AHP. Additionally, a method that uses fuzzy standard additive model (FSAM) for cancer classification based on genes selected by AHP is also proposed in this paper. Traditional FSAM learning is a hybrid process comprising unsupervised structure learning and supervised parameter tuning. Genetic algorithm (GA) is incorporated in-between unsupervised and supervised training to optimize the number of fuzzy rules. The integration of GA enables FSAM to deal with the high-dimensional-low-sample nature of microarray data and thus enhance the efficiency of the classification. Experiments are carried out on numerous microarray datasets. Results demonstrate the performance dominance of the AHP-based gene selection against the single ranking methods. Furthermore, the combination of AHP-FSAM shows a great accuracy in microarray data classification compared to various competing classifiers. The proposed approach therefore is useful for medical practitioners and clinicians as a decision support system that can be implemented in the real medical practice. PMID:25823003

Hierarchical gene selection and genetic fuzzy system for cancer microarray data classification.

PubMed

Nguyen, Thanh; Khosravi, Abbas; Creighton, Douglas; Nahavandi, Saeid

2015-01-01

This paper introduces a novel approach to gene selection based on a substantial modification of analytic hierarchy process (AHP). The modified AHP systematically integrates outcomes of individual filter methods to select the most informative genes for microarray classification. Five individual ranking methods including t-test, entropy, receiver operating characteristic (ROC) curve, Wilcoxon and signal to noise ratio are employed to rank genes. These ranked genes are then considered as inputs for the modified AHP. Additionally, a method that uses fuzzy standard additive model (FSAM) for cancer classification based on genes selected by AHP is also proposed in this paper. Traditional FSAM learning is a hybrid process comprising unsupervised structure learning and supervised parameter tuning. Genetic algorithm (GA) is incorporated in-between unsupervised and supervised training to optimize the number of fuzzy rules. The integration of GA enables FSAM to deal with the high-dimensional-low-sample nature of microarray data and thus enhance the efficiency of the classification. Experiments are carried out on numerous microarray datasets. Results demonstrate the performance dominance of the AHP-based gene selection against the single ranking methods. Furthermore, the combination of AHP-FSAM shows a great accuracy in microarray data classification compared to various competing classifiers. The proposed approach therefore is useful for medical practitioners and clinicians as a decision support system that can be implemented in the real medical practice.

Determining geophysical properties from well log data using artificial neural networks and fuzzy inference systems

NASA Astrophysics Data System (ADS)

Chang, Hsien-Cheng

Two novel synergistic systems consisting of artificial neural networks and fuzzy inference systems are developed to determine geophysical properties by using well log data. These systems are employed to improve the determination accuracy in carbonate rocks, which are generally more complex than siliciclastic rocks. One system, consisting of a single adaptive resonance theory (ART) neural network and three fuzzy inference systems (FISs), is used to determine the permeability category. The other system, which is composed of three ART neural networks and a single FIS, is employed to determine the lithofacies. The geophysical properties studied in this research, permeability category and lithofacies, are treated as categorical data. The permeability values are transformed into a "permeability category" to account for the effects of scale differences between core analyses and well logs, and heterogeneity in the carbonate rocks. The ART neural networks dynamically cluster the input data sets into different groups. The FIS is used to incorporate geologic experts' knowledge, which is usually in linguistic forms, into systems. These synergistic systems thus provide viable alternative solutions to overcome the effects of heterogeneity, the uncertainties of carbonate rock depositional environments, and the scarcity of well log data. The results obtained in this research show promising improvements over backpropagation neural networks. For the permeability category, the prediction accuracies are 68.4% and 62.8% for the multiple-single ART neural network-FIS and a single backpropagation neural network, respectively. For lithofacies, the prediction accuracies are 87.6%, 79%, and 62.8% for the single-multiple ART neural network-FIS, a single ART neural network, and a single backpropagation neural network, respectively. The sensitivity analysis results show that the multiple-single ART neural networks-FIS and a single ART neural network possess the same matching trends in

A new algorithm to find fuzzy Hamilton cycle in a fuzzy network using adjacency matrix and minimum vertex degree.

PubMed

Nagoor Gani, A; Latha, S R

2016-01-01

A Hamiltonian cycle in a graph is a cycle that visits each node/vertex exactly once. A graph containing a Hamiltonian cycle is called a Hamiltonian graph. There have been several researches to find the number of Hamiltonian cycles of a Hamilton graph. As the number of vertices and edges grow, it becomes very difficult to keep track of all the different ways through which the vertices are connected. Hence, analysis of large graphs can be efficiently done with the assistance of a computer system that interprets graphs as matrices. And, of course, a good and well written algorithm will expedite the analysis even faster. The most convenient way to quickly test whether there is an edge between two vertices is to represent graphs using adjacent matrices. In this paper, a new algorithm is proposed to find fuzzy Hamiltonian cycle using adjacency matrix and the degree of the vertices of a fuzzy graph. A fuzzy graph structure is also modeled to illustrate the proposed algorithms with the selected air network of Indigo airlines.

A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation

PubMed Central

Tahmasebi, Pejman; Hezarkhani, Ardeshir

2012-01-01

The grade estimation is a quite important and money/time-consuming stage in a mine project, which is considered as a challenge for the geologists and mining engineers due to the structural complexities in mineral ore deposits. To overcome this problem, several artificial intelligence techniques such as Artificial Neural Networks (ANN) and Fuzzy Logic (FL) have recently been employed with various architectures and properties. However, due to the constraints of both methods, they yield the desired results only under the specific circumstances. As an example, one major problem in FL is the difficulty of constructing the membership functions (MFs).Other problems such as architecture and local minima could also be located in ANN designing. Therefore, a new methodology is presented in this paper for grade estimation. This method which is based on ANN and FL is called “Coactive Neuro-Fuzzy Inference System” (CANFIS) which combines two approaches, ANN and FL. The combination of these two artificial intelligence approaches is achieved via the verbal and numerical power of intelligent systems. To improve the performance of this system, a Genetic Algorithm (GA) – as a well-known technique to solve the complex optimization problems – is also employed to optimize the network parameters including learning rate, momentum of the network and the number of MFs for each input. A comparison of these techniques (ANN, Adaptive Neuro-Fuzzy Inference System or ANFIS) with this new method (CANFIS–GA) is also carried out through a case study in Sungun copper deposit, located in East-Azerbaijan, Iran. The results show that CANFIS–GA could be a faster and more accurate alternative to the existing time-consuming methodologies for ore grade estimation and that is, therefore, suggested to be applied for grade estimation in similar problems. PMID:25540468

A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation

NASA Astrophysics Data System (ADS)

Tahmasebi, Pejman; Hezarkhani, Ardeshir

2012-05-01

The grade estimation is a quite important and money/time-consuming stage in a mine project, which is considered as a challenge for the geologists and mining engineers due to the structural complexities in mineral ore deposits. To overcome this problem, several artificial intelligence techniques such as Artificial Neural Networks (ANN) and Fuzzy Logic (FL) have recently been employed with various architectures and properties. However, due to the constraints of both methods, they yield the desired results only under the specific circumstances. As an example, one major problem in FL is the difficulty of constructing the membership functions (MFs).Other problems such as architecture and local minima could also be located in ANN designing. Therefore, a new methodology is presented in this paper for grade estimation. This method which is based on ANN and FL is called "Coactive Neuro-Fuzzy Inference System" (CANFIS) which combines two approaches, ANN and FL. The combination of these two artificial intelligence approaches is achieved via the verbal and numerical power of intelligent systems. To improve the performance of this system, a Genetic Algorithm (GA) - as a well-known technique to solve the complex optimization problems - is also employed to optimize the network parameters including learning rate, momentum of the network and the number of MFs for each input. A comparison of these techniques (ANN, Adaptive Neuro-Fuzzy Inference System or ANFIS) with this new method (CANFIS-GA) is also carried out through a case study in Sungun copper deposit, located in East-Azerbaijan, Iran. The results show that CANFIS-GA could be a faster and more accurate alternative to the existing time-consuming methodologies for ore grade estimation and that is, therefore, suggested to be applied for grade estimation in similar problems.

Fuzzy Logic Control Based QoS Management in Wireless Sensor/Actuator Networks

PubMed Central

Xia, Feng; Zhao, Wenhong; Sun, Youxian; Tian, Yu-Chu

2007-01-01

Wireless sensor/actuator networks (WSANs) are emerging rapidly as a new generation of sensor networks. Despite intensive research in wireless sensor networks (WSNs), limited work has been found in the open literature in the field of WSANs. In particular, quality-of-service (QoS) management in WSANs remains an important issue yet to be investigated. As an attempt in this direction, this paper develops a fuzzy logic control based QoS management (FLC-QM) scheme for WSANs with constrained resources and in dynamic and unpredictable environments. Taking advantage of the feedback control technology, this scheme deals with the impact of unpredictable changes in traffic load on the QoS of WSANs. It utilizes a fuzzy logic controller inside each source sensor node to adapt sampling period to the deadline miss ratio associated with data transmission from the sensor to the actuator. The deadline miss ratio is maintained at a pre-determined desired level so that the required QoS can be achieved. The FLC-QM has the advantages of generality, scalability, and simplicity. Simulation results show that the FLC-QM can provide WSANs with QoS support. PMID:28903288

Adaptive fuzzy wavelet network control of second order multi-agent systems with unknown nonlinear dynamics.

PubMed

Taheri, Mehdi; Sheikholeslam, Farid; Najafi, Majddedin; Zekri, Maryam

2017-07-01

In this paper, consensus problem is considered for second order multi-agent systems with unknown nonlinear dynamics under undirected graphs. A novel distributed control strategy is suggested for leaderless systems based on adaptive fuzzy wavelet networks. Adaptive fuzzy wavelet networks are employed to compensate for the effect of unknown nonlinear dynamics. Moreover, the proposed method is developed for leader following systems and leader following systems with state time delays. Lyapunov functions are applied to prove uniformly ultimately bounded stability of closed loop systems and to obtain adaptive laws. Three simulation examples are presented to illustrate the effectiveness of the proposed control algorithms. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Clinical Outcome Prediction in Aneurysmal Subarachnoid Hemorrhage Using Bayesian Neural Networks with Fuzzy Logic Inferences

PubMed Central

Lo, Benjamin W. Y.; Macdonald, R. Loch; Baker, Andrew; Levine, Mitchell A. H.

2013-01-01

Objective. The novel clinical prediction approach of Bayesian neural networks with fuzzy logic inferences is created and applied to derive prognostic decision rules in cerebral aneurysmal subarachnoid hemorrhage (aSAH). Methods. The approach of Bayesian neural networks with fuzzy logic inferences was applied to data from five trials of Tirilazad for aneurysmal subarachnoid hemorrhage (3551 patients). Results. Bayesian meta-analyses of observational studies on aSAH prognostic factors gave generalizable posterior distributions of population mean log odd ratios (ORs). Similar trends were noted in Bayesian and linear regression ORs. Significant outcome predictors include normal motor response, cerebral infarction, history of myocardial infarction, cerebral edema, history of diabetes mellitus, fever on day 8, prior subarachnoid hemorrhage, admission angiographic vasospasm, neurological grade, intraventricular hemorrhage, ruptured aneurysm size, history of hypertension, vasospasm day, age and mean arterial pressure. Heteroscedasticity was present in the nontransformed dataset. Artificial neural networks found nonlinear relationships with 11 hidden variables in 1 layer, using the multilayer perceptron model. Fuzzy logic decision rules (centroid defuzzification technique) denoted cut-off points for poor prognosis at greater than 2.5 clusters. Discussion. This aSAH prognostic system makes use of existing knowledge, recognizes unknown areas, incorporates one's clinical reasoning, and compensates for uncertainty in prognostication. PMID:23690884

A decentralized fuzzy C-means-based energy-efficient routing protocol for wireless sensor networks.

PubMed

Alia, Osama Moh'd

2014-01-01

Energy conservation in wireless sensor networks (WSNs) is a vital consideration when designing wireless networking protocols. In this paper, we propose a Decentralized Fuzzy Clustering Protocol, named DCFP, which minimizes total network energy dissipation to promote maximum network lifetime. The process of constructing the infrastructure for a given WSN is performed only once at the beginning of the protocol at a base station, which remains unchanged throughout the network's lifetime. In this initial construction step, a fuzzy C-means algorithm is adopted to allocate sensor nodes into their most appropriate clusters. Subsequently, the protocol runs its rounds where each round is divided into a CH-Election phase and a Data Transmission phase. In the CH-Election phase, the election of new cluster heads is done locally in each cluster where a new multicriteria objective function is proposed to enhance the quality of elected cluster heads. In the Data Transmission phase, the sensing and data transmission from each sensor node to their respective cluster head is performed and cluster heads in turn aggregate and send the sensed data to the base station. Simulation results demonstrate that the proposed protocol improves network lifetime, data delivery, and energy consumption compared to other well-known energy-efficient protocols.

A Decentralized Fuzzy C-Means-Based Energy-Efficient Routing Protocol for Wireless Sensor Networks

PubMed Central

2014-01-01

Energy conservation in wireless sensor networks (WSNs) is a vital consideration when designing wireless networking protocols. In this paper, we propose a Decentralized Fuzzy Clustering Protocol, named DCFP, which minimizes total network energy dissipation to promote maximum network lifetime. The process of constructing the infrastructure for a given WSN is performed only once at the beginning of the protocol at a base station, which remains unchanged throughout the network's lifetime. In this initial construction step, a fuzzy C-means algorithm is adopted to allocate sensor nodes into their most appropriate clusters. Subsequently, the protocol runs its rounds where each round is divided into a CH-Election phase and a Data Transmission phase. In the CH-Election phase, the election of new cluster heads is done locally in each cluster where a new multicriteria objective function is proposed to enhance the quality of elected cluster heads. In the Data Transmission phase, the sensing and data transmission from each sensor node to their respective cluster head is performed and cluster heads in turn aggregate and send the sensed data to the base station. Simulation results demonstrate that the proposed protocol improves network lifetime, data delivery, and energy consumption compared to other well-known energy-efficient protocols. PMID:25162060

SOS based robust H(∞) fuzzy dynamic output feedback control of nonlinear networked control systems.

PubMed

Chae, Seunghwan; Nguang, Sing Kiong

2014-07-01

In this paper, a methodology for designing a fuzzy dynamic output feedback controller for discrete-time nonlinear networked control systems is presented where the nonlinear plant is modelled by a Takagi-Sugeno fuzzy model and the network-induced delays by a finite state Markov process. The transition probability matrix for the Markov process is allowed to be partially known, providing a more practical consideration of the real world. Furthermore, the fuzzy controller's membership functions and premise variables are not assumed to be the same as the plant's membership functions and premise variables, that is, the proposed approach can handle the case, when the premise of the plant are not measurable or delayed. The membership functions of the plant and the controller are approximated as polynomial functions, then incorporated into the controller design. Sufficient conditions for the existence of the controller are derived in terms of sum of square inequalities, which are then solved by YALMIP. Finally, a numerical example is used to demonstrate the validity of the proposed methodology.

A novel stock forecasting model based on High-order-fuzzy-fluctuation Trends and Back Propagation Neural Network

PubMed Central

Dai, Zongli; Zhao, Aiwu; He, Jie

2018-01-01

In this paper, we propose a hybrid method to forecast the stock prices called High-order-fuzzy-fluctuation-Trends-based Back Propagation(HTBP)Neural Network model. First, we compare each value of the historical training data with the previous day's value to obtain a fluctuation trend time series (FTTS). On this basis, the FTTS blur into fuzzy time series (FFTS) based on the fluctuation of the increasing, equality, decreasing amplitude and direction. Since the relationship between FFTS and future wave trends is nonlinear, the HTBP neural network algorithm is used to find the mapping rules in the form of self-learning. Finally, the results of the algorithm output are used to predict future fluctuations. The proposed model provides some innovative features:(1)It combines fuzzy set theory and neural network algorithm to avoid overfitting problems existed in traditional models. (2)BP neural network algorithm can intelligently explore the internal rules of the actual existence of sequential data, without the need to analyze the influence factors of specific rules and the path of action. (3)The hybrid modal can reasonably remove noises from the internal rules by proper fuzzy treatment. This paper takes the TAIEX data set of Taiwan stock exchange as an example, and compares and analyzes the prediction performance of the model. The experimental results show that this method can predict the stock market in a very simple way. At the same time, we use this method to predict the Shanghai stock exchange composite index, and further verify the effectiveness and universality of the method. PMID:29420584

A novel stock forecasting model based on High-order-fuzzy-fluctuation Trends and Back Propagation Neural Network.

PubMed

Guan, Hongjun; Dai, Zongli; Zhao, Aiwu; He, Jie

2018-01-01

In this paper, we propose a hybrid method to forecast the stock prices called High-order-fuzzy-fluctuation-Trends-based Back Propagation(HTBP)Neural Network model. First, we compare each value of the historical training data with the previous day's value to obtain a fluctuation trend time series (FTTS). On this basis, the FTTS blur into fuzzy time series (FFTS) based on the fluctuation of the increasing, equality, decreasing amplitude and direction. Since the relationship between FFTS and future wave trends is nonlinear, the HTBP neural network algorithm is used to find the mapping rules in the form of self-learning. Finally, the results of the algorithm output are used to predict future fluctuations. The proposed model provides some innovative features:(1)It combines fuzzy set theory and neural network algorithm to avoid overfitting problems existed in traditional models. (2)BP neural network algorithm can intelligently explore the internal rules of the actual existence of sequential data, without the need to analyze the influence factors of specific rules and the path of action. (3)The hybrid modal can reasonably remove noises from the internal rules by proper fuzzy treatment. This paper takes the TAIEX data set of Taiwan stock exchange as an example, and compares and analyzes the prediction performance of the model. The experimental results show that this method can predict the stock market in a very simple way. At the same time, we use this method to predict the Shanghai stock exchange composite index, and further verify the effectiveness and universality of the method.

Predicting subcontractor performance using web-based Evolutionary Fuzzy Neural Networks.

PubMed

Ko, Chien-Ho

2013-01-01

Subcontractor performance directly affects project success. The use of inappropriate subcontractors may result in individual work delays, cost overruns, and quality defects throughout the project. This study develops web-based Evolutionary Fuzzy Neural Networks (EFNNs) to predict subcontractor performance. EFNNs are a fusion of Genetic Algorithms (GAs), Fuzzy Logic (FL), and Neural Networks (NNs). FL is primarily used to mimic high level of decision-making processes and deal with uncertainty in the construction industry. NNs are used to identify the association between previous performance and future status when predicting subcontractor performance. GAs are optimizing parameters required in FL and NNs. EFNNs encode FL and NNs using floating numbers to shorten the length of a string. A multi-cut-point crossover operator is used to explore the parameter and retain solution legality. Finally, the applicability of the proposed EFNNs is validated using real subcontractors. The EFNNs are evolved using 22 historical patterns and tested using 12 unseen cases. Application results show that the proposed EFNNs surpass FL and NNs in predicting subcontractor performance. The proposed approach improves prediction accuracy and reduces the effort required to predict subcontractor performance, providing field operators with web-based remote access to a reliable, scientific prediction mechanism.

Predicting Subcontractor Performance Using Web-Based Evolutionary Fuzzy Neural Networks

PubMed Central

2013-01-01

Subcontractor performance directly affects project success. The use of inappropriate subcontractors may result in individual work delays, cost overruns, and quality defects throughout the project. This study develops web-based Evolutionary Fuzzy Neural Networks (EFNNs) to predict subcontractor performance. EFNNs are a fusion of Genetic Algorithms (GAs), Fuzzy Logic (FL), and Neural Networks (NNs). FL is primarily used to mimic high level of decision-making processes and deal with uncertainty in the construction industry. NNs are used to identify the association between previous performance and future status when predicting subcontractor performance. GAs are optimizing parameters required in FL and NNs. EFNNs encode FL and NNs using floating numbers to shorten the length of a string. A multi-cut-point crossover operator is used to explore the parameter and retain solution legality. Finally, the applicability of the proposed EFNNs is validated using real subcontractors. The EFNNs are evolved using 22 historical patterns and tested using 12 unseen cases. Application results show that the proposed EFNNs surpass FL and NNs in predicting subcontractor performance. The proposed approach improves prediction accuracy and reduces the effort required to predict subcontractor performance, providing field operators with web-based remote access to a reliable, scientific prediction mechanism. PMID:23864830

Hydrological time series modeling: A comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques

NASA Astrophysics Data System (ADS)

Lohani, A. K.; Kumar, Rakesh; Singh, R. D.

2012-06-01

SummaryTime series modeling is necessary for the planning and management of reservoirs. More recently, the soft computing techniques have been used in hydrological modeling and forecasting. In this study, the potential of artificial neural networks and neuro-fuzzy system in monthly reservoir inflow forecasting are examined by developing and comparing monthly reservoir inflow prediction models, based on autoregressive (AR), artificial neural networks (ANNs) and adaptive neural-based fuzzy inference system (ANFIS). To take care the effect of monthly periodicity in the flow data, cyclic terms are also included in the ANN and ANFIS models. Working with time series flow data of the Sutlej River at Bhakra Dam, India, several ANN and adaptive neuro-fuzzy models are trained with different input vectors. To evaluate the performance of the selected ANN and adaptive neural fuzzy inference system (ANFIS) models, comparison is made with the autoregressive (AR) models. The ANFIS model trained with the input data vector including previous inflows and cyclic terms of monthly periodicity has shown a significant improvement in the forecast accuracy in comparison with the ANFIS models trained with the input vectors considering only previous inflows. In all cases ANFIS gives more accurate forecast than the AR and ANN models. The proposed ANFIS model coupled with the cyclic terms is shown to provide better representation of the monthly inflow forecasting for planning and operation of reservoir.

Fuzzy control of magnetic bearings

NASA Technical Reports Server (NTRS)

Feeley, J. J.; Niederauer, G. M.; Ahlstrom, D. J.

1991-01-01

The use of an adaptive fuzzy control algorithm implemented on a VLSI chip for the control of a magnetic bearing was considered. The architecture of the adaptive fuzzy controller is similar to that of a neural network. The performance of the fuzzy controller is compared to that of a conventional controller by computer simulation.

A Comparison of Neural Networks and Fuzzy Logic Methods for Process Modeling

NASA Technical Reports Server (NTRS)

Cios, Krzysztof J.; Sala, Dorel M.; Berke, Laszlo

1996-01-01

The goal of this work was to analyze the potential of neural networks and fuzzy logic methods to develop approximate response surfaces as process modeling, that is for mapping of input into output. Structural response was chosen as an example. Each of the many methods surveyed are explained and the results are presented. Future research directions are also discussed.

Neuro-fuzzy controller to navigate an unmanned vehicle.

PubMed

Selma, Boumediene; Chouraqui, Samira

2013-12-01

A Neuro-fuzzy control method for an Unmanned Vehicle (UV) simulation is described. The objective is guiding an autonomous vehicle to a desired destination along a desired path in an environment characterized by a terrain and a set of distinct objects, such as obstacles like donkey traffic lights and cars circulating in the trajectory. The autonomous navigate ability and road following precision are mainly influenced by its control strategy and real-time control performance. Fuzzy Logic Controller can very well describe the desired system behavior with simple "if-then" relations owing the designer to derive "if-then" rules manually by trial and error. On the other hand, Neural Networks perform function approximation of a system but cannot interpret the solution obtained neither check if its solution is plausible. The two approaches are complementary. Combining them, Neural Networks will allow learning capability while Fuzzy-Logic will bring knowledge representation (Neuro-Fuzzy). In this paper, an artificial neural network fuzzy inference system (ANFIS) controller is described and implemented to navigate the autonomous vehicle. Results show several improvements in the control system adjusted by neuro-fuzzy techniques in comparison to the previous methods like Artificial Neural Network (ANN).

An optimal general type-2 fuzzy controller for Urban Traffic Network.

PubMed

Khooban, Mohammad Hassan; Vafamand, Navid; Liaghat, Alireza; Dragicevic, Tomislav

2017-01-01

Urban traffic network model is illustrated by state-charts and object-diagram. However, they have limitations to show the behavioral perspective of the Traffic Information flow. Consequently, a state space model is used to calculate the half-value waiting time of vehicles. In this study, a combination of the general type-2 fuzzy logic sets and the Modified Backtracking Search Algorithm (MBSA) techniques are used in order to control the traffic signal scheduling and phase succession so as to guarantee a smooth flow of traffic with the least wait times and average queue length. The parameters of input and output membership functions are optimized simultaneously by the novel heuristic algorithm MBSA. A comparison is made between the achieved results with those of optimal and conventional type-1 fuzzy logic controllers. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Effectiveness evaluation of double-layered satellite network with laser and microwave hybrid links based on fuzzy analytic hierarchy process

NASA Astrophysics Data System (ADS)

Zhang, Wei; Rao, Qiaomeng

2018-01-01

In order to solve the problem of high speed, large capacity and limited spectrum resources of satellite communication network, a double-layered satellite network with global seamless coverage based on laser and microwave hybrid links is proposed in this paper. By analyzing the characteristics of the double-layered satellite network with laser and microwave hybrid links, an effectiveness evaluation index system for the network is established. And then, the fuzzy analytic hierarchy process, which combines the analytic hierarchy process and the fuzzy comprehensive evaluation theory, is used to evaluate the effectiveness of the double-layered satellite network with laser and microwave hybrid links. Furthermore, the evaluation result of the proposed hybrid link network is obtained by simulation. The effectiveness evaluation process of the proposed double-layered satellite network with laser and microwave hybrid links can help to optimize the design of hybrid link double-layered satellite network and improve the operating efficiency of the satellite system.

Multilayer perceptron, fuzzy sets, and classification

NASA Technical Reports Server (NTRS)

Pal, Sankar K.; Mitra, Sushmita

1992-01-01

A fuzzy neural network model based on the multilayer perceptron, using the back-propagation algorithm, and capable of fuzzy classification of patterns is described. The input vector consists of membership values to linguistic properties while the output vector is defined in terms of fuzzy class membership values. This allows efficient modeling of fuzzy or uncertain patterns with appropriate weights being assigned to the backpropagated errors depending upon the membership values at the corresponding outputs. During training, the learning rate is gradually decreased in discrete steps until the network converges to a minimum error solution. The effectiveness of the algorithm is demonstrated on a speech recognition problem. The results are compared with those of the conventional MLP, the Bayes classifier, and the other related models.

Fuzzy-neural control of an aircraft tracking camera platform

NASA Technical Reports Server (NTRS)

Mcgrath, Dennis

1994-01-01

A fuzzy-neural control system simulation was developed for the control of a camera platform used to observe aircraft on final approach to an aircraft carrier. The fuzzy-neural approach to control combines the structure of a fuzzy knowledge base with a supervised neural network's ability to adapt and improve. The performance characteristics of this hybrid system were compared to those of a fuzzy system and a neural network system developed independently to determine if the fusion of these two technologies offers any advantage over the use of one or the other. The results of this study indicate that the fuzzy-neural approach to control offers some advantages over either fuzzy or neural control alone.

Fuzzy-driven energy storage system for mitigating voltage unbalance factor on distribution network with photovoltaic system

NASA Astrophysics Data System (ADS)

Wong, Jianhui; Lim, Yun Seng; Morris, Stella; Morris, Ezra; Chua, Kein Huat

2017-04-01

The amount of small-scaled renewable energy sources is anticipated to increase on the low-voltage distribution networks for the improvement of energy efficiency and reduction of greenhouse gas emission. The growth of the PV systems on the low-voltage distribution networks can create voltage unbalance, voltage rise, and reverse-power flow. Usually these issues happen with little fluctuation. However, it tends to fluctuate severely as Malaysia is a region with low clear sky index. A large amount of clouds often passes over the country, hence making the solar irradiance to be highly scattered. Therefore, the PV power output fluctuates substantially. These issues can lead to the malfunction of the electronic based equipment, reduction in the network efficiency and improper operation of the power protection system. At the current practice, the amount of PV system installed on the distribution network is constraint by the utility company. As a result, this can limit the reduction of carbon footprint. Therefore, energy storage system is proposed as a solution for these power quality issues. To ensure an effective operation of the distribution network with PV system, a fuzzy control system is developed and implemented to govern the operation of an energy storage system. The fuzzy driven energy storage system is able to mitigate the fluctuating voltage rise and voltage unbalance on the electrical grid by actively manipulates the flow of real power between the grid and the batteries. To verify the effectiveness of the proposed fuzzy driven energy storage system, an experimental network integrated with 7.2kWp PV system was setup. Several case studies are performed to evaluate the response of the proposed solution to mitigate voltage rises, voltage unbalance and reduce the amount of reverse power flow under highly intermittent PV power output.

Relationship between isoseismal area and magnitude of historical earthquakes in Greece by a hybrid fuzzy neural network method

NASA Astrophysics Data System (ADS)

Tselentis, G.-A.; Sokos, E.

2012-01-01

In this paper we suggest the use of diffusion-neural-networks, (neural networks with intrinsic fuzzy logic abilities) to assess the relationship between isoseismal area and earthquake magnitude for the region of Greece. It is of particular importance to study historical earthquakes for which we often have macroseismic information in the form of isoseisms but it is statistically incomplete to assess magnitudes from an isoseismal area or to train conventional artificial neural networks for magnitude estimation. Fuzzy relationships are developed and used to train a feed forward neural network with a back propagation algorithm to obtain the final relationships. Seismic intensity data from 24 earthquakes in Greece have been used. Special attention is being paid to the incompleteness and contradictory patterns in scanty historical earthquake records. The results show that the proposed processing model is very effective, better than applying classical artificial neural networks since the magnitude macroseismic intensity target function has a strong nonlinearity and in most cases the macroseismic datasets are very small.

Identification of Abnormal System Noise Temperature Patterns in Deep Space Network Antennas Using Neural Network Trained Fuzzy Logic

NASA Technical Reports Server (NTRS)

Lu, Thomas; Pham, Timothy; Liao, Jason

2011-01-01

This paper presents the development of a fuzzy logic function trained by an artificial neural network to classify the system noise temperature (SNT) of antennas in the NASA Deep Space Network (DSN). The SNT data were classified into normal, marginal, and abnormal classes. The irregular SNT pattern was further correlated with link margin and weather data. A reasonably good correlation is detected among high SNT, low link margin and the effect of bad weather; however we also saw some unexpected non-correlations which merit further study in the future.

Gene network biological validity based on gene-gene interaction relevance.

PubMed

Gómez-Vela, Francisco; Díaz-Díaz, Norberto

2014-01-01

In recent years, gene networks have become one of the most useful tools for modeling biological processes. Many inference gene network algorithms have been developed as techniques for extracting knowledge from gene expression data. Ensuring the reliability of the inferred gene relationships is a crucial task in any study in order to prove that the algorithms used are precise. Usually, this validation process can be carried out using prior biological knowledge. The metabolic pathways stored in KEGG are one of the most widely used knowledgeable sources for analyzing relationships between genes. This paper introduces a new methodology, GeneNetVal, to assess the biological validity of gene networks based on the relevance of the gene-gene interactions stored in KEGG metabolic pathways. Hence, a complete KEGG pathway conversion into a gene association network and a new matching distance based on gene-gene interaction relevance are proposed. The performance of GeneNetVal was established with three different experiments. Firstly, our proposal is tested in a comparative ROC analysis. Secondly, a randomness study is presented to show the behavior of GeneNetVal when the noise is increased in the input network. Finally, the ability of GeneNetVal to detect biological functionality of the network is shown.

Application of fuzzy neural network technologies in management of transport and logistics processes in Arctic

NASA Astrophysics Data System (ADS)

Levchenko, N. G.; Glushkov, S. V.; Sobolevskaya, E. Yu; Orlov, A. P.

2018-05-01

The method of modeling the transport and logistics process using fuzzy neural network technologies has been considered. The analysis of the implemented fuzzy neural network model of the information management system of transnational multimodal transportation of the process showed the expediency of applying this method to the management of transport and logistics processes in the Arctic and Subarctic conditions. The modular architecture of this model can be expanded by incorporating additional modules, since the working conditions in the Arctic and the subarctic themselves will present more and more realistic tasks. The architecture allows increasing the information management system, without affecting the system or the method itself. The model has a wide range of application possibilities, including: analysis of the situation and behavior of interacting elements; dynamic monitoring and diagnostics of management processes; simulation of real events and processes; prediction and prevention of critical situations.

Interference Path Loss Prediction in A319/320 Airplanes Using Modulated Fuzzy Logic and Neural Networks

NASA Technical Reports Server (NTRS)

Jafri, Madiha J.; Ely, Jay J.; Vahala, Linda L.

2007-01-01

In this paper, neural network (NN) modeling is combined with fuzzy logic to estimate Interference Path Loss measurements on Airbus 319 and 320 airplanes. Interference patterns inside the aircraft are classified and predicted based on the locations of the doors, windows, aircraft structures and the communication/navigation system-of-concern. Modeled results are compared with measured data. Combining fuzzy logic and NN modeling is shown to improve estimates of measured data over estimates obtained with NN alone. A plan is proposed to enhance the modeling for better prediction of electromagnetic coupling problems inside aircraft.

Adaptive Fuzzy Systems in Computational Intelligence

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.

1996-01-01

In recent years, the interest in computational intelligence techniques, which currently includes neural networks, fuzzy systems, and evolutionary programming, has grown significantly and a number of their applications have been developed in the government and industry. In future, an essential element in these systems will be fuzzy systems that can learn from experience by using neural network in refining their performances. The GARIC architecture, introduced earlier, is an example of a fuzzy reinforcement learning system which has been applied in several control domains such as cart-pole balancing, simulation of to Space Shuttle orbital operations, and tether control. A number of examples from GARIC's applications in these domains will be demonstrated.

An object recognition method based on fuzzy theory and BP networks

NASA Astrophysics Data System (ADS)

Wu, Chuan; Zhu, Ming; Yang, Dong

2006-01-01

It is difficult to choose eigenvectors when neural network recognizes object. It is possible that the different object eigenvectors is similar or the same object eigenvectors is different under scaling, shifting, rotation if eigenvectors can not be chosen appropriately. In order to solve this problem, the image is edged, the membership function is reconstructed and a new threshold segmentation method based on fuzzy theory is proposed to get the binary image. Moment invariant of binary image is extracted and normalized. Some time moment invariant is too small to calculate effectively so logarithm of moment invariant is taken as input eigenvectors of BP network. The experimental results demonstrate that the proposed approach could recognize the object effectively, correctly and quickly.

Using heuristic algorithms for capacity leasing and task allocation issues in telecommunication networks under fuzzy quality of service constraints

NASA Astrophysics Data System (ADS)

Huseyin Turan, Hasan; Kasap, Nihat; Savran, Huseyin

2014-03-01

Nowadays, every firm uses telecommunication networks in different amounts and ways in order to complete their daily operations. In this article, we investigate an optimisation problem that a firm faces when acquiring network capacity from a market in which there exist several network providers offering different pricing and quality of service (QoS) schemes. The QoS level guaranteed by network providers and the minimum quality level of service, which is needed for accomplishing the operations are denoted as fuzzy numbers in order to handle the non-deterministic nature of the telecommunication network environment. Interestingly, the mathematical formulation of the aforementioned problem leads to the special case of a well-known two-dimensional bin packing problem, which is famous for its computational complexity. We propose two different heuristic solution procedures that have the capability of solving the resulting nonlinear mixed integer programming model with fuzzy constraints. In conclusion, the efficiency of each algorithm is tested in several test instances to demonstrate the applicability of the methodology.

Application of a fuzzy neural network model in predicting polycyclic aromatic hydrocarbon-mediated perturbations of the Cyp1b1 transcriptional regulatory network in mouse skin

DOE Office of Scientific and Technical Information (OSTI.GOV)

Larkin, Andrew; Department of Statistics, Oregon State University; Superfund Research Center, Oregon State University

2013-03-01

Polycyclic aromatic hydrocarbons (PAHs) are present in the environment as complex mixtures with components that have diverse carcinogenic potencies and mostly unknown interactive effects. Non-additive PAH interactions have been observed in regulation of cytochrome P450 (CYP) gene expression in the CYP1 family. To better understand and predict biological effects of complex mixtures, such as environmental PAHs, an 11 gene input-1 gene output fuzzy neural network (FNN) was developed for predicting PAH-mediated perturbations of dermal Cyp1b1 transcription in mice. Input values were generalized using fuzzy logic into low, medium, and high fuzzy subsets, and sorted using k-means clustering to create Mamdanimore » logic functions for predicting Cyp1b1 mRNA expression. Model testing was performed with data from microarray analysis of skin samples from FVB/N mice treated with toluene (vehicle control), dibenzo[def,p]chrysene (DBC), benzo[a]pyrene (BaP), or 1 of 3 combinations of diesel particulate extract (DPE), coal tar extract (CTE) and cigarette smoke condensate (CSC) using leave-one-out cross-validation. Predictions were within 1 log{sub 2} fold change unit of microarray data, with the exception of the DBC treatment group, where the unexpected down-regulation of Cyp1b1 expression was predicted but did not reach statistical significance on the microarrays. Adding CTE to DPE was predicted to increase Cyp1b1 expression, whereas adding CSC to CTE and DPE was predicted to have no effect, in agreement with microarray results. The aryl hydrocarbon receptor repressor (Ahrr) was determined to be the most significant input variable for model predictions using back-propagation and normalization of FNN weights. - Highlights: ► Tested a model to predict PAH mixture-mediated changes in Cyp1b1 expression ► Quantitative predictions in agreement with microarrays for Cyp1b1 induction ► Unexpected difference in expression between DBC and other treatments predicted ► Model

Fuzzy Bayesian Network-Bow-Tie Analysis of Gas Leakage during Biomass Gasification

PubMed Central

Yan, Fang; Xu, Kaili; Yao, Xiwen; Li, Yang

2016-01-01

Biomass gasification technology has been rapidly developed recently. But fire and poisoning accidents caused by gas leakage restrict the development and promotion of biomass gasification. Therefore, probabilistic safety assessment (PSA) is necessary for biomass gasification system. Subsequently, Bayesian network-bow-tie (BN-bow-tie) analysis was proposed by mapping bow-tie analysis into Bayesian network (BN). Causes of gas leakage and the accidents triggered by gas leakage can be obtained by bow-tie analysis, and BN was used to confirm the critical nodes of accidents by introducing corresponding three importance measures. Meanwhile, certain occurrence probability of failure was needed in PSA. In view of the insufficient failure data of biomass gasification, the occurrence probability of failure which cannot be obtained from standard reliability data sources was confirmed by fuzzy methods based on expert judgment. An improved approach considered expert weighting to aggregate fuzzy numbers included triangular and trapezoidal numbers was proposed, and the occurrence probability of failure was obtained. Finally, safety measures were indicated based on the obtained critical nodes. The theoretical occurrence probabilities in one year of gas leakage and the accidents caused by it were reduced to 1/10.3 of the original values by these safety measures. PMID:27463975

Effect of Aggregation Operators on Network-Based Disease Gene Prioritization: A Case Study on Blood Disorders.

PubMed

Grewal, Nivit; Singh, Shailendra; Chand, Trilok

2017-01-01

Owing to the innate noise in the biological data sources, a single source or a single measure do not suffice for an effective disease gene prioritization. So, the integration of multiple data sources or aggregation of multiple measures is the need of the hour. The aggregation operators combine multiple related data values to a single value such that the combined value has the effect of all the individual values. In this paper, an attempt has been made for applying the fuzzy aggregation on the network-based disease gene prioritization and investigate its effect under noise conditions. This study has been conducted for a set of 15 blood disorders by fusing four different network measures, computed from the protein interaction network, using a selected set of aggregation operators and ranking the genes on the basis of the aggregated value. The aggregation operator-based rankings have been compared with the "Random walk with restart" gene prioritization method. The impact of noise has also been investigated by adding varying proportions of noise to the seed set. The results reveal that for all the selected blood disorders, the Mean of Maximal operator has relatively outperformed the other aggregation operators for noisy as well as non-noisy data.

Fuzzy Logic based Handoff Latency Reduction Mechanism in Layer 2 of Heterogeneous Mobile IPv6 Networks

NASA Astrophysics Data System (ADS)

Anwar, Farhat; Masud, Mosharrof H.; Latif, Suhaimi A.

2013-12-01

Mobile IPv6 (MIPv6) is one of the pioneer standards that support mobility in IPv6 environment. It has been designed to support different types of technologies for providing seamless communications in next generation network. However, MIPv6 and subsequent standards have some limitations due to its handoff latency. In this paper, a fuzzy logic based mechanism is proposed to reduce the handoff latency of MIPv6 for Layer 2 (L2) by scanning the Access Points (APs) while the Mobile Node (MN) is moving among different APs. Handoff latency occurs when the MN switches from one AP to another in L2. Heterogeneous network is considered in this research in order to reduce the delays in L2. Received Signal Strength Indicator (RSSI) and velocity of the MN are considered as the input of fuzzy logic technique. This technique helps the MN to measure optimum signal quality from APs for the speedy mobile node based on fuzzy logic input rules and makes a list of interfaces. A suitable interface from the list of available interfaces can be selected like WiFi, WiMAX or GSM. Simulation results show 55% handoff latency reduction and 50% packet loss improvement in L2 compared to standard to MIPv6.

Optoelectronic fuzzy associative memory with controllable attraction basin sizes

NASA Astrophysics Data System (ADS)

Wen, Zhiqing; Campbell, Scott; Wu, Weishu; Yeh, Pochi

1995-10-01

We propose and demonstrate a new fuzzy associative memory model that provides an option to control the sizes of the attraction basins in neural networks. In our optoelectronic implementation we use spatial/polarization encoding to represent the fuzzy variables. Shadow casting of the encoded patterns is employed to yield the fuzzy-absolute difference between fuzzy variables.

A fuzzy adaptive network approach to parameter estimation in cases where independent variables come from an exponential distribution

NASA Astrophysics Data System (ADS)

Dalkilic, Turkan Erbay; Apaydin, Aysen

2009-11-01

In a regression analysis, it is assumed that the observations come from a single class in a data cluster and the simple functional relationship between the dependent and independent variables can be expressed using the general model; Y=f(X)+[epsilon]. However; a data cluster may consist of a combination of observations that have different distributions that are derived from different clusters. When faced with issues of estimating a regression model for fuzzy inputs that have been derived from different distributions, this regression model has been termed the [`]switching regression model' and it is expressed with . Here li indicates the class number of each independent variable and p is indicative of the number of independent variables [J.R. Jang, ANFIS: Adaptive-network-based fuzzy inference system, IEEE Transaction on Systems, Man and Cybernetics 23 (3) (1993) 665-685; M. Michel, Fuzzy clustering and switching regression models using ambiguity and distance rejects, Fuzzy Sets and Systems 122 (2001) 363-399; E.Q. Richard, A new approach to estimating switching regressions, Journal of the American Statistical Association 67 (338) (1972) 306-310]. In this study, adaptive networks have been used to construct a model that has been formed by gathering obtained models. There are methods that suggest the class numbers of independent variables heuristically. Alternatively, in defining the optimal class number of independent variables, the use of suggested validity criterion for fuzzy clustering has been aimed. In the case that independent variables have an exponential distribution, an algorithm has been suggested for defining the unknown parameter of the switching regression model and for obtaining the estimated values after obtaining an optimal membership function, which is suitable for exponential distribution.

Discovering Implicit Entity Relation with the Gene-Citation-Gene Network

PubMed Central

Song, Min; Han, Nam-Gi; Kim, Yong-Hwan; Ding, Ying; Chambers, Tamy

2013-01-01

In this paper, we apply the entitymetrics model to our constructed Gene-Citation-Gene (GCG) network. Based on the premise there is a hidden, but plausible, relationship between an entity in one article and an entity in its citing article, we constructed a GCG network of gene pairs implicitly connected through citation. We compare the performance of this GCG network to a gene-gene (GG) network constructed over the same corpus but which uses gene pairs explicitly connected through traditional co-occurrence. Using 331,411 MEDLINE abstracts collected from 18,323 seed articles and their references, we identify 25 gene pairs. A comparison of these pairs with interactions found in BioGRID reveal that 96% of the gene pairs in the GCG network have known interactions. We measure network performance using degree, weighted degree, closeness, betweenness centrality and PageRank. Combining all measures, we find the GCG network has more gene pairs, but a lower matching rate than the GG network. However, combining top ranked genes in both networks produces a matching rate of 35.53%. By visualizing both the GG and GCG networks, we find that cancer is the most dominant disease associated with the genes in both networks. Overall, the study indicates that the GCG network can be useful for detecting gene interaction in an implicit manner. PMID:24358368

Fuzzy-information-based robustness of interconnected networks against attacks and failures

NASA Astrophysics Data System (ADS)

Zhu, Qian; Zhu, Zhiliang; Wang, Yifan; Yu, Hai

2016-09-01

Cascading failure is fatal in applications and its investigation is essential and therefore became a focal topic in the field of complex networks in the last decade. In this paper, a cascading failure model is established for interconnected networks and the associated data-packet transport problem is discussed. A distinguished feature of the new model is its utilization of fuzzy information in resisting uncertain failures and malicious attacks. We numerically find that the giant component of the network after failures increases with tolerance parameter for any coupling preference and attacking ambiguity. Moreover, considering the effect of the coupling probability on the robustness of the networks, we find that the robustness of the assortative coupling and random coupling of the network model increases with the coupling probability. However, for disassortative coupling, there exists a critical phenomenon for coupling probability. In addition, a critical value that attacking information accuracy affects the network robustness is observed. Finally, as a practical example, the interconnected AS-level Internet in South Korea and Japan is analyzed. The actual data validates the theoretical model and analytic results. This paper thus provides some guidelines for preventing cascading failures in the design of architecture and optimization of real-world interconnected networks.

A neuro-fuzzy architecture for real-time applications

NASA Technical Reports Server (NTRS)

Ramamoorthy, P. A.; Huang, Song

1992-01-01

Neural networks and fuzzy expert systems perform the same task of functional mapping using entirely different approaches. Each approach has certain unique features. The ability to learn specific input-output mappings from large input/output data possibly corrupted by noise and the ability to adapt or continue learning are some important features of neural networks. Fuzzy expert systems are known for their ability to deal with fuzzy information and incomplete/imprecise data in a structured, logical way. Since both of these techniques implement the same task (that of functional mapping--we regard 'inferencing' as one specific category under this class), a fusion of the two concepts that retains their unique features while overcoming their individual drawbacks will have excellent applications in the real world. In this paper, we arrive at a new architecture by fusing the two concepts. The architecture has the trainability/adaptibility (based on input/output observations) property of the neural networks and the architectural features that are unique to fuzzy expert systems. It also does not require specific information such as fuzzy rules, defuzzification procedure used, etc., though any such information can be integrated into the architecture. We show that this architecture can provide better performance than is possible from a single two or three layer feedforward neural network. Further, we show that this new architecture can be used as an efficient vehicle for hardware implementation of complex fuzzy expert systems for real-time applications. A numerical example is provided to show the potential of this approach.

BP network identification technology of infrared polarization based on fuzzy c-means clustering

NASA Astrophysics Data System (ADS)

Zeng, Haifang; Gu, Guohua; He, Weiji; Chen, Qian; Yang, Wei

2011-08-01

Infrared detection system is frequently employed on surveillance operations and reconnaissance mission to detect particular targets of interest in both civilian and military communities. By incorporating the polarization of light as supplementary information, the target discrimination performance could be enhanced. So this paper proposed an infrared target identification method which is based on fuzzy theory and neural network with polarization properties of targets. The paper utilizes polarization degree and light intensity to advance the unsupervised KFCM (kernel fuzzy C-Means) clustering method. And establish different material pol1arization properties database. In the built network, the system can feedback output corresponding material types of probability distribution toward any input polarized degree such as 10° 15°, 20°, 25°, 30°. KFCM, which has stronger robustness and accuracy than FCM, introduces kernel idea and gives the noise points and invalid value different but intuitively reasonable weights. Because of differences in characterization of material properties, there will be some conflicts in classification results. And D - S evidence theory was used in the combination of the polarization and intensity information. Related results show KFCM clustering precision and operation rate are higher than that of the FCM clustering method. The artificial neural network method realizes material identification, which reasonable solved the problems of complexity in environmental information of infrared polarization, and improperness of background knowledge and inference rule. This method of polarization identification is fast in speed, good in self-adaption and high in resolution.

GENE EXPRESSION NETWORKS

EPA Science Inventory

"Gene expression network" is the term used to describe the interplay, simple or complex, between two or more gene products in performing a specific cellular function. Although the delineation of such networks is complicated by the existence of multiple and subtle types of intera...

Fuzzy Neural Network-Based Interacting Multiple Model for Multi-Node Target Tracking Algorithm

PubMed Central

Sun, Baoliang; Jiang, Chunlan; Li, Ming

2016-01-01

An interacting multiple model for multi-node target tracking algorithm was proposed based on a fuzzy neural network (FNN) to solve the multi-node target tracking problem of wireless sensor networks (WSNs). Measured error variance was adaptively adjusted during the multiple model interacting output stage using the difference between the theoretical and estimated values of the measured error covariance matrix. The FNN fusion system was established during multi-node fusion to integrate with the target state estimated data from different nodes and consequently obtain network target state estimation. The feasibility of the algorithm was verified based on a network of nine detection nodes. Experimental results indicated that the proposed algorithm could trace the maneuvering target effectively under sensor failure and unknown system measurement errors. The proposed algorithm exhibited great practicability in the multi-node target tracking of WSNs. PMID:27809271

Design of fuzzy system by NNs and realization of adaptability

NASA Technical Reports Server (NTRS)

Takagi, Hideyuki

1993-01-01

The issue of designing and tuning fuzzy membership functions by neural networks (NN's) was started by NN-driven Fuzzy Reasoning in 1988. NN-driven fuzzy reasoning involves a NN embedded in the fuzzy system which generates membership values. In conventional fuzzy system design, the membership functions are hand-crafted by trial and error for each input variable. In contrast, NN-driven fuzzy reasoning considers several variables simultaneously and can design a multidimensional, nonlinear membership function for the entire subspace.

A fuzzy neural network sliding mode controller for vibration suppression in robotically assisted minimally invasive surgery.

PubMed

Sang, Hongqiang; Yang, Chenghao; Liu, Fen; Yun, Jintian; Jin, Guoguang

2016-12-01

It is very important for robotically assisted minimally invasive surgery to achieve a high-precision and smooth motion control. However, the surgical instrument tip will exhibit vibration caused by nonlinear friction and unmodeled dynamics, especially when the surgical robot system is attempting low-speed, fine motion. A fuzzy neural network sliding mode controller (FNNSMC) is proposed to suppress vibration of the surgical robotic system. Nonlinear friction and modeling uncertainties are compensated by a Stribeck model, a radial basis function (RBF) neural network and a fuzzy system, respectively. Simulations and experiments were performed on a 3 degree-of-freedom (DOF) minimally invasive surgical robot. The results demonstrate that the FNNSMC is effective and can suppress vibrations at the surgical instrument tip. The proposed FNNSMC can provide a robust performance and suppress the vibrations at the surgical instrument tip, which can enhance the quality and security of surgical procedures. Copyright © 2016 John Wiley & Sons, Ltd.

Nonlinear aeroacoustic characterization of Helmholtz resonators with a local-linear neuro-fuzzy network model

NASA Astrophysics Data System (ADS)

Förner, K.; Polifke, W.

2017-10-01

The nonlinear acoustic behavior of Helmholtz resonators is characterized by a data-based reduced-order model, which is obtained by a combination of high-resolution CFD simulation and system identification. It is shown that even in the nonlinear regime, a linear model is capable of describing the reflection behavior at a particular amplitude with quantitative accuracy. This observation motivates to choose a local-linear model structure for this study, which consists of a network of parallel linear submodels. A so-called fuzzy-neuron layer distributes the input signal over the linear submodels, depending on the root mean square of the particle velocity at the resonator surface. The resulting model structure is referred to as an local-linear neuro-fuzzy network. System identification techniques are used to estimate the free parameters of this model from training data. The training data are generated by CFD simulations of the resonator, with persistent acoustic excitation over a wide range of frequencies and sound pressure levels. The estimated nonlinear, reduced-order models show good agreement with CFD and experimental data over a wide range of amplitudes for several test cases.

A hybrid intelligent algorithm for portfolio selection problem with fuzzy returns

NASA Astrophysics Data System (ADS)

Li, Xiang; Zhang, Yang; Wong, Hau-San; Qin, Zhongfeng

2009-11-01

Portfolio selection theory with fuzzy returns has been well developed and widely applied. Within the framework of credibility theory, several fuzzy portfolio selection models have been proposed such as mean-variance model, entropy optimization model, chance constrained programming model and so on. In order to solve these nonlinear optimization models, a hybrid intelligent algorithm is designed by integrating simulated annealing algorithm, neural network and fuzzy simulation techniques, where the neural network is used to approximate the expected value and variance for fuzzy returns and the fuzzy simulation is used to generate the training data for neural network. Since these models are used to be solved by genetic algorithm, some comparisons between the hybrid intelligent algorithm and genetic algorithm are given in terms of numerical examples, which imply that the hybrid intelligent algorithm is robust and more effective. In particular, it reduces the running time significantly for large size problems.

Petri Nets with Fuzzy Logic (PNFL): Reverse Engineering and Parametrization

PubMed Central

Küffner, Robert; Petri, Tobias; Windhager, Lukas; Zimmer, Ralf

2010-01-01

Background The recent DREAM4 blind assessment provided a particularly realistic and challenging setting for network reverse engineering methods. The in silico part of DREAM4 solicited the inference of cycle-rich gene regulatory networks from heterogeneous, noisy expression data including time courses as well as knockout, knockdown and multifactorial perturbations. Methodology and Principal Findings We inferred and parametrized simulation models based on Petri Nets with Fuzzy Logic (PNFL). This completely automated approach correctly reconstructed networks with cycles as well as oscillating network motifs. PNFL was evaluated as the best performer on DREAM4 in silico networks of size 10 with an area under the precision-recall curve (AUPR) of 81%. Besides topology, we inferred a range of additional mechanistic details with good reliability, e.g. distinguishing activation from inhibition as well as dependent from independent regulation. Our models also performed well on new experimental conditions such as double knockout mutations that were not included in the provided datasets. Conclusions The inference of biological networks substantially benefits from methods that are expressive enough to deal with diverse datasets in a unified way. At the same time, overly complex approaches could generate multiple different models that explain the data equally well. PNFL appears to strike the balance between expressive power and complexity. This also applies to the intuitive representation of PNFL models combining a straightforward graphical notation with colloquial fuzzy parameters. PMID:20862218

Predicting Length of Stay in Intensive Care Units after Cardiac Surgery: Comparison of Artificial Neural Networks and Adaptive Neuro-fuzzy System.

PubMed

Maharlou, Hamidreza; Niakan Kalhori, Sharareh R; Shahbazi, Shahrbanoo; Ravangard, Ramin

2018-04-01

Accurate prediction of patients' length of stay is highly important. This study compared the performance of artificial neural network and adaptive neuro-fuzzy system algorithms to predict patients' length of stay in intensive care units (ICU) after cardiac surgery. A cross-sectional, analytical, and applied study was conducted. The required data were collected from 311 cardiac patients admitted to intensive care units after surgery at three hospitals of Shiraz, Iran, through a non-random convenience sampling method during the second quarter of 2016. Following the initial processing of influential factors, models were created and evaluated. The results showed that the adaptive neuro-fuzzy algorithm (with mean squared error [MSE] = 7 and R = 0.88) resulted in the creation of a more precise model than the artificial neural network (with MSE = 21 and R = 0.60). The adaptive neuro-fuzzy algorithm produces a more accurate model as it applies both the capabilities of a neural network architecture and experts' knowledge as a hybrid algorithm. It identifies nonlinear components, yielding remarkable results for prediction the length of stay, which is a useful calculation output to support ICU management, enabling higher quality of administration and cost reduction.

Leveraging multiple gene networks to prioritize GWAS candidate genes via network representation learning.

PubMed

Wu, Mengmeng; Zeng, Wanwen; Liu, Wenqiang; Lv, Hairong; Chen, Ting; Jiang, Rui

2018-06-03

Genome-wide association studies (GWAS) have successfully discovered a number of disease-associated genetic variants in the past decade, providing an unprecedented opportunity for deciphering genetic basis of human inherited diseases. However, it is still a challenging task to extract biological knowledge from the GWAS data, due to such issues as missing heritability and weak interpretability. Indeed, the fact that the majority of discovered loci fall into noncoding regions without clear links to genes has been preventing the characterization of their functions and appealing for a sophisticated approach to bridge genetic and genomic studies. Towards this problem, network-based prioritization of candidate genes, which performs integrated analysis of gene networks with GWAS data, has emerged as a promising direction and attracted much attention. However, most existing methods overlook the sparse and noisy properties of gene networks and thus may lead to suboptimal performance. Motivated by this understanding, we proposed a novel method called REGENT for integrating multiple gene networks with GWAS data to prioritize candidate genes for complex diseases. We leveraged a technique called the network representation learning to embed a gene network into a compact and robust feature space, and then designed a hierarchical statistical model to integrate features of multiple gene networks with GWAS data for the effective inference of genes associated with a disease of interest. We applied our method to six complex diseases and demonstrated the superior performance of REGENT over existing approaches in recovering known disease-associated genes. We further conducted a pathway analysis and showed that the ability of REGENT to discover disease-associated pathways. We expect to see applications of our method to a broad spectrum of diseases for post-GWAS analysis. REGENT is freely available at https://github.com/wmmthu/REGENT. Copyright © 2018 Elsevier Inc. All rights reserved.

Cloud E-Learning Service Strategies for Improving E-Learning Innovation Performance in a Fuzzy Environment by Using a New Hybrid Fuzzy Multiple Attribute Decision-Making Model

ERIC Educational Resources Information Center

Su, Chiu Hung; Tzeng, Gwo-Hshiung; Hu, Shu-Kung

2016-01-01

The purpose of this study was to address this problem by applying a new hybrid fuzzy multiple criteria decision-making model including (a) using the fuzzy decision-making trial and evaluation laboratory (DEMATEL) technique to construct the fuzzy scope influential network relationship map (FSINRM) and determine the fuzzy influential weights of the…

Learning fuzzy logic control system

NASA Technical Reports Server (NTRS)

Lung, Leung Kam

1994-01-01

The performance of the Learning Fuzzy Logic Control System (LFLCS), developed in this thesis, has been evaluated. The Learning Fuzzy Logic Controller (LFLC) learns to control the motor by learning the set of teaching values that are generated by a classical PI controller. It is assumed that the classical PI controller is tuned to minimize the error of a position control system of the D.C. motor. The Learning Fuzzy Logic Controller developed in this thesis is a multi-input single-output network. Training of the Learning Fuzzy Logic Controller is implemented off-line. Upon completion of the training process (using Supervised Learning, and Unsupervised Learning), the LFLC replaces the classical PI controller. In this thesis, a closed loop position control system of a D.C. motor using the LFLC is implemented. The primary focus is on the learning capabilities of the Learning Fuzzy Logic Controller. The learning includes symbolic representation of the Input Linguistic Nodes set and Output Linguistic Notes set. In addition, we investigate the knowledge-based representation for the network. As part of the design process, we implement a digital computer simulation of the LFLCS. The computer simulation program is written in 'C' computer language, and it is implemented in DOS platform. The LFLCS, designed in this thesis, has been developed on a IBM compatible 486-DX2 66 computer. First, the performance of the Learning Fuzzy Logic Controller is evaluated by comparing the angular shaft position of the D.C. motor controlled by a conventional PI controller and that controlled by the LFLC. Second, the symbolic representation of the LFLC and the knowledge-based representation for the network are investigated by observing the parameters of the Fuzzy Logic membership functions and the links at each layer of the LFLC. While there are some limitations of application with this approach, the result of the simulation shows that the LFLC is able to control the angular shaft position of the

Recognition of pigment network pattern in dermoscopy images based on fuzzy classification of pixels.

PubMed

Garcia-Arroyo, Jose Luis; Garcia-Zapirain, Begonya

2018-01-01

One of the most relevant dermoscopic patterns is the pigment network. An innovative method of pattern recognition is presented for its detection in dermoscopy images. It consists of two steps. In the first one, by means of a supervised machine learning process and after performing the extraction of different colour and texture features, a fuzzy classification of pixels into the three categories present in the pattern's definition ("net", "hole" and "other") is carried out. This enables the three corresponding fuzzy sets to be created and, as a result, the three probability images that map them out are generated. In the second step, the pigment network pattern is characterised from a parameterisation process -derived from the system specification- and the subsequent extraction of different features calculated from the combinations of image masks extracted from the probability images, corresponding to the alpha-cuts obtained from the fuzzy sets. The method was tested on a database of 875 images -by far the largest used in the state of the art to detect pigment network- extracted from a public Atlas of Dermoscopy, obtaining AUC results of 0.912 and 88%% accuracy, with 90.71%% sensitivity and 83.44%% specificity. The main contribution of this method is the very design of the algorithm, highly innovative, which could also be used to deal with other pattern recognition problems of a similar nature. Other contributions are: 1. The good performance in discriminating between the pattern and the disturbing artefacts -which means that no prior preprocessing is required in this method- and between the pattern and other dermoscopic patterns; 2. It puts forward a new methodological approach for work of this kind, introducing the system specification as a required step prior to algorithm design and development, being this specification the basis for a required parameterisation -in the form of configurable parameters (with their value ranges) and set threshold values- of the

Evolutionary fuzzy ARTMAP neural networks for classification of semiconductor defects.

PubMed

Tan, Shing Chiang; Watada, Junzo; Ibrahim, Zuwairie; Khalid, Marzuki

2015-05-01

Wafer defect detection using an intelligent system is an approach of quality improvement in semiconductor manufacturing that aims to enhance its process stability, increase production capacity, and improve yields. Occasionally, only few records that indicate defective units are available and they are classified as a minority group in a large database. Such a situation leads to an imbalanced data set problem, wherein it engenders a great challenge to deal with by applying machine-learning techniques for obtaining effective solution. In addition, the database may comprise overlapping samples of different classes. This paper introduces two models of evolutionary fuzzy ARTMAP (FAM) neural networks to deal with the imbalanced data set problems in a semiconductor manufacturing operations. In particular, both the FAM models and hybrid genetic algorithms are integrated in the proposed evolutionary artificial neural networks (EANNs) to classify an imbalanced data set. In addition, one of the proposed EANNs incorporates a facility to learn overlapping samples of different classes from the imbalanced data environment. The classification results of the proposed evolutionary FAM neural networks are presented, compared, and analyzed using several classification metrics. The outcomes positively indicate the effectiveness of the proposed networks in handling classification problems with imbalanced data sets.

Evaluating water management strategies in watersheds by new hybrid Fuzzy Analytical Network Process (FANP) methods

NASA Astrophysics Data System (ADS)

RazaviToosi, S. L.; Samani, J. M. V.

2016-03-01

Watersheds are considered as hydrological units. Their other important aspects such as economic, social and environmental functions play crucial roles in sustainable development. The objective of this work is to develop methodologies to prioritize watersheds by considering different development strategies in environmental, social and economic sectors. This ranking could play a significant role in management to assign the most critical watersheds where by employing water management strategies, best condition changes are expected to be accomplished. Due to complex relations among different criteria, two new hybrid fuzzy ANP (Analytical Network Process) algorithms, fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and fuzzy max-min set methods are used to provide more flexible and accurate decision model. Five watersheds in Iran named Oroomeyeh, Atrak, Sefidrood, Namak and Zayandehrood are considered as alternatives. Based on long term development goals, 38 water management strategies are defined as subcriteria in 10 clusters. The main advantage of the proposed methods is its ability to overcome uncertainty. This task is accomplished by using fuzzy numbers in all steps of the algorithms. To validate the proposed method, the final results were compared with those obtained from the ANP algorithm and the Spearman rank correlation coefficient is applied to find the similarity in the different ranking methods. Finally, the sensitivity analysis was conducted to investigate the influence of cluster weights on the final ranking.

Learning fuzzy information in a hybrid connectionist, symbolic model

NASA Technical Reports Server (NTRS)

Romaniuk, Steve G.; Hall, Lawrence O.

1993-01-01

An instance-based learning system is presented. SC-net is a fuzzy hybrid connectionist, symbolic learning system. It remembers some examples and makes groups of examples into exemplars. All real-valued attributes are represented as fuzzy sets. The network representation and learning method is described. To illustrate this approach to learning in fuzzy domains, an example of segmenting magnetic resonance images of the brain is discussed. Clearly, the boundaries between human tissues are ill-defined or fuzzy. Example fuzzy rules for recognition are generated. Segmentations are presented that provide results that radiologists find useful.

Fuzzy neural network technique for system state forecasting.

PubMed

Li, Dezhi; Wang, Wilson; Ismail, Fathy

2013-10-01

In many system state forecasting applications, the prediction is performed based on multiple datasets, each corresponding to a distinct system condition. The traditional methods dealing with multiple datasets (e.g., vector autoregressive moving average models and neural networks) have some shortcomings, such as limited modeling capability and opaque reasoning operations. To tackle these problems, a novel fuzzy neural network (FNN) is proposed in this paper to effectively extract information from multiple datasets, so as to improve forecasting accuracy. The proposed predictor consists of both autoregressive (AR) nodes modeling and nonlinear nodes modeling; AR models/nodes are used to capture the linear correlation of the datasets, and the nonlinear correlation of the datasets are modeled with nonlinear neuron nodes. A novel particle swarm technique [i.e., Laplace particle swarm (LPS) method] is proposed to facilitate parameters estimation of the predictor and improve modeling accuracy. The effectiveness of the developed FNN predictor and the associated LPS method is verified by a series of tests related to Mackey-Glass data forecast, exchange rate data prediction, and gear system prognosis. Test results show that the developed FNN predictor and the LPS method can capture the dynamics of multiple datasets effectively and track system characteristics accurately.

Adaptive fuzzy system for 3-D vision

NASA Technical Reports Server (NTRS)

Mitra, Sunanda

1993-01-01

An adaptive fuzzy system using the concept of the Adaptive Resonance Theory (ART) type neural network architecture and incorporating fuzzy c-means (FCM) system equations for reclassification of cluster centers was developed. The Adaptive Fuzzy Leader Clustering (AFLC) architecture is a hybrid neural-fuzzy system which learns on-line in a stable and efficient manner. The system uses a control structure similar to that found in the Adaptive Resonance Theory (ART-1) network to identify the cluster centers initially. The initial classification of an input takes place in a two stage process; a simple competitive stage and a distance metric comparison stage. The cluster prototypes are then incrementally updated by relocating the centroid positions from Fuzzy c-Means (FCM) system equations for the centroids and the membership values. The operational characteristics of AFLC and the critical parameters involved in its operation are discussed. The performance of the AFLC algorithm is presented through application of the algorithm to the Anderson Iris data, and laser-luminescent fingerprint image data. The AFLC algorithm successfully classifies features extracted from real data, discrete or continuous, indicating the potential strength of this new clustering algorithm in analyzing complex data sets. The hybrid neuro-fuzzy AFLC algorithm will enhance analysis of a number of difficult recognition and control problems involved with Tethered Satellite Systems and on-orbit space shuttle attitude controller.

A New Efficient Hybrid Intelligent Model for Biodegradation Process of DMP with Fuzzy Wavelet Neural Networks

PubMed Central

Huang, Mingzhi; Zhang, Tao; Ruan, Jujun; Chen, Xiaohong

2017-01-01

A new efficient hybrid intelligent approach based on fuzzy wavelet neural network (FWNN) was proposed for effectively modeling and simulating biodegradation process of Dimethyl phthalate (DMP) in an anaerobic/anoxic/oxic (AAO) wastewater treatment process. With the self learning and memory abilities of neural networks (NN), handling uncertainty capacity of fuzzy logic (FL), analyzing local details superiority of wavelet transform (WT) and global search of genetic algorithm (GA), the proposed hybrid intelligent model can extract the dynamic behavior and complex interrelationships from various water quality variables. For finding the optimal values for parameters of the proposed FWNN, a hybrid learning algorithm integrating an improved genetic optimization and gradient descent algorithm is employed. The results show, compared with NN model (optimized by GA) and kinetic model, the proposed FWNN model have the quicker convergence speed, the higher prediction performance, and smaller RMSE (0.080), MSE (0.0064), MAPE (1.8158) and higher R2 (0.9851) values. which illustrates FWNN model simulates effluent DMP more accurately than the mechanism model. PMID:28120889

A New Efficient Hybrid Intelligent Model for Biodegradation Process of DMP with Fuzzy Wavelet Neural Networks

NASA Astrophysics Data System (ADS)

Huang, Mingzhi; Zhang, Tao; Ruan, Jujun; Chen, Xiaohong

2017-01-01

A new efficient hybrid intelligent approach based on fuzzy wavelet neural network (FWNN) was proposed for effectively modeling and simulating biodegradation process of Dimethyl phthalate (DMP) in an anaerobic/anoxic/oxic (AAO) wastewater treatment process. With the self learning and memory abilities of neural networks (NN), handling uncertainty capacity of fuzzy logic (FL), analyzing local details superiority of wavelet transform (WT) and global search of genetic algorithm (GA), the proposed hybrid intelligent model can extract the dynamic behavior and complex interrelationships from various water quality variables. For finding the optimal values for parameters of the proposed FWNN, a hybrid learning algorithm integrating an improved genetic optimization and gradient descent algorithm is employed. The results show, compared with NN model (optimized by GA) and kinetic model, the proposed FWNN model have the quicker convergence speed, the higher prediction performance, and smaller RMSE (0.080), MSE (0.0064), MAPE (1.8158) and higher R2 (0.9851) values. which illustrates FWNN model simulates effluent DMP more accurately than the mechanism model.

Fuzzy Logic, Neural Networks, Genetic Algorithms: Views of Three Artificial Intelligence Concepts Used in Modeling Scientific Systems

ERIC Educational Resources Information Center

Sunal, Cynthia Szymanski; Karr, Charles L.; Sunal, Dennis W.

2003-01-01

Students' conceptions of three major artificial intelligence concepts used in the modeling of systems in science, fuzzy logic, neural networks, and genetic algorithms were investigated before and after a higher education science course. Students initially explored their prior ideas related to the three concepts through active tasks. Then,…

Brain Dynamics in Predicting Driving Fatigue Using a Recurrent Self-Evolving Fuzzy Neural Network.

PubMed

Liu, Yu-Ting; Lin, Yang-Yin; Wu, Shang-Lin; Chuang, Chun-Hsiang; Lin, Chin-Teng

2016-02-01

This paper proposes a generalized prediction system called a recurrent self-evolving fuzzy neural network (RSEFNN) that employs an on-line gradient descent learning rule to address the electroencephalography (EEG) regression problem in brain dynamics for driving fatigue. The cognitive states of drivers significantly affect driving safety; in particular, fatigue driving, or drowsy driving, endangers both the individual and the public. For this reason, the development of brain-computer interfaces (BCIs) that can identify drowsy driving states is a crucial and urgent topic of study. Many EEG-based BCIs have been developed as artificial auxiliary systems for use in various practical applications because of the benefits of measuring EEG signals. In the literature, the efficacy of EEG-based BCIs in recognition tasks has been limited by low resolutions. The system proposed in this paper represents the first attempt to use the recurrent fuzzy neural network (RFNN) architecture to increase adaptability in realistic EEG applications to overcome this bottleneck. This paper further analyzes brain dynamics in a simulated car driving task in a virtual-reality environment. The proposed RSEFNN model is evaluated using the generalized cross-subject approach, and the results indicate that the RSEFNN is superior to competing models regardless of the use of recurrent or nonrecurrent structures.

GUI Type Fault Diagnostic Program for a Turboshaft Engine Using Fuzzy and Neural Networks

NASA Astrophysics Data System (ADS)

Kong, Changduk; Koo, Youngju

2011-04-01

The helicopter to be operated in a severe flight environmental condition must have a very reliable propulsion system. On-line condition monitoring and fault detection of the engine can promote reliability and availability of the helicopter propulsion system. A hybrid health monitoring program using Fuzzy Logic and Neural Network Algorithms can be proposed. In this hybrid method, the Fuzzy Logic identifies easily the faulted components from engine measuring parameter changes, and the Neural Networks can quantify accurately its identified faults. In order to use effectively the fault diagnostic system, a GUI (Graphical User Interface) type program is newly proposed. This program is composed of the real time monitoring part, the engine condition monitoring part and the fault diagnostic part. The real time monitoring part can display measuring parameters of the study turboshaft engine such as power turbine inlet temperature, exhaust gas temperature, fuel flow, torque and gas generator speed. The engine condition monitoring part can evaluate the engine condition through comparison between monitoring performance parameters the base performance parameters analyzed by the base performance analysis program using look-up tables. The fault diagnostic part can identify and quantify the single faults the multiple faults from the monitoring parameters using hybrid method.

Study on a Biometric Authentication Model based on ECG using a Fuzzy Neural Network

NASA Astrophysics Data System (ADS)

Kim, Ho J.; Lim, Joon S.

2018-03-01

Traditional authentication methods use numbers or graphic passwords and thus involve the risk of loss or theft. Various studies are underway regarding biometric authentication because it uses the unique biometric data of a human being. Biometric authentication technology using ECG from biometric data involves signals that record electrical stimuli from the heart. It is difficult to manipulate and is advantageous in that it enables unrestrained measurements from sensors that are attached to the skin. This study is on biometric authentication methods using the neural network with weighted fuzzy membership functions (NEWFM). In the biometric authentication process, normalization and the ensemble average is applied during preprocessing, characteristics are extracted using Haar-wavelets, and a registration process called “training” is performed in the fuzzy neural network. In the experiment, biometric authentication was performed on 73 subjects in the Physionet Database. 10-40 ECG waveforms were tested for use in the registration process, and 15 ECG waveforms were deemed the appropriate number for registering ECG waveforms. 1 ECG waveforms were used during the authentication stage to conduct the biometric authentication test. Upon testing the proposed biometric authentication method based on 73 subjects from the Physionet Database, the TAR was 98.32% and FAR was 5.84%.

Fuzzy logic, artificial neural network and mathematical model for prediction of white mulberry drying kinetics

NASA Astrophysics Data System (ADS)

Jahedi Rad, Shahpour; Kaveh, Mohammad; Sharabiani, Vali Rasooli; Taghinezhad, Ebrahim

2018-05-01

The thin-layer convective- infrared drying behavior of white mulberry was experimentally studied at infrared power levels of 500, 1000 and 1500 W, drying air temperatures of 40, 55 and 70 °C and inlet drying air speeds of 0.4, 1 and 1.6 m/s. Drying rate raised with the rise of infrared power levels at a distinct air temperature and velocity and thus decreased the drying time. Five mathematical models describing thin-layer drying have been fitted to the drying data. Midlli et al. model could satisfactorily describe the convective-infrared drying of white mulberry fruit with the values of the correlation coefficient (R 2=0.9986) and root mean square error of (RMSE= 0.04795). Artificial neural network (ANN) and fuzzy logic methods was desirably utilized for modeling output parameters (moisture ratio (MR)) regarding input parameters. Results showed that output parameters were more accurately predicted by fuzzy model than by the ANN and mathematical models. Correlation coefficient (R 2) and RMSE generated by the fuzzy model (respectively 0.9996 and 0.01095) were higher than referred values for the ANN model (0.9990 and 0.01988 respectively).

Reconstructing directed gene regulatory network by only gene expression data.

PubMed

Zhang, Lu; Feng, Xi Kang; Ng, Yen Kaow; Li, Shuai Cheng

2016-08-18

Accurately identifying gene regulatory network is an important task in understanding in vivo biological activities. The inference of such networks is often accomplished through the use of gene expression data. Many methods have been developed to evaluate gene expression dependencies between transcription factor and its target genes, and some methods also eliminate transitive interactions. The regulatory (or edge) direction is undetermined if the target gene is also a transcription factor. Some methods predict the regulatory directions in the gene regulatory networks by locating the eQTL single nucleotide polymorphism, or by observing the gene expression changes when knocking out/down the candidate transcript factors; regrettably, these additional data are usually unavailable, especially for the samples deriving from human tissues. In this study, we propose the Context Based Dependency Network (CBDN), a method that is able to infer gene regulatory networks with the regulatory directions from gene expression data only. To determine the regulatory direction, CBDN computes the influence of source to target by evaluating the magnitude changes of expression dependencies between the target gene and the others with conditioning on the source gene. CBDN extends the data processing inequality by involving the dependency direction to distinguish between direct and transitive relationship between genes. We also define two types of important regulators which can influence a majority of the genes in the network directly or indirectly. CBDN can detect both of these two types of important regulators by averaging the influence functions of candidate regulator to the other genes. In our experiments with simulated and real data, even with the regulatory direction taken into account, CBDN outperforms the state-of-the-art approaches for inferring gene regulatory network. CBDN identifies the important regulators in the predicted network: 1. TYROBP influences a batch of genes that are

A study of structural properties of gene network graphs for mathematical modeling of integrated mosaic gene networks.

PubMed

Petrovskaya, Olga V; Petrovskiy, Evgeny D; Lavrik, Inna N; Ivanisenko, Vladimir A

2017-04-01

Gene network modeling is one of the widely used approaches in systems biology. It allows for the study of complex genetic systems function, including so-called mosaic gene networks, which consist of functionally interacting subnetworks. We conducted a study of a mosaic gene networks modeling method based on integration of models of gene subnetworks by linear control functionals. An automatic modeling of 10,000 synthetic mosaic gene regulatory networks was carried out using computer experiments on gene knockdowns/knockouts. Structural analysis of graphs of generated mosaic gene regulatory networks has revealed that the most important factor for building accurate integrated mathematical models, among those analyzed in the study, is data on expression of genes corresponding to the vertices with high properties of centrality.

a New Model for Fuzzy Personalized Route Planning Using Fuzzy Linguistic Preference Relation

NASA Astrophysics Data System (ADS)

Nadi, S.; Houshyaripour, A. H.

2017-09-01

This paper proposes a new model for personalized route planning under uncertain condition. Personalized routing, involves different sources of uncertainty. These uncertainties can be raised from user's ambiguity about their preferences, imprecise criteria values and modelling process. The proposed model uses Fuzzy Linguistic Preference Relation Analytical Hierarchical Process (FLPRAHP) to analyse user's preferences under uncertainty. Routing is a multi-criteria task especially in transportation networks, where the users wish to optimize their routes based on different criteria. However, due to the lake of knowledge about the preferences of different users and uncertainties available in the criteria values, we propose a new personalized fuzzy routing method based on the fuzzy ranking using center of gravity. The model employed FLPRAHP method to aggregate uncertain criteria values regarding uncertain user's preferences while improve consistency with least possible comparisons. An illustrative example presents the effectiveness and capability of the proposed model to calculate best personalize route under fuzziness and uncertainty.

Fuzzy Neural Classifiers for Multi-Wavelength Interdigital Sensors

NASA Astrophysics Data System (ADS)

Xenides, D.; Vlachos, D. S.; Simos, T. E.

2007-12-01

The use of multi-wavelength interdigital sensors for non-destructive testing is based on the capability of the measuring system to classify the measured impendence according to some physical properties of the material under test. By varying the measuring frequency and the wavelength of the sensor (and thus the penetration depth of the electric field inside the material under test) we can produce images that correspond to various configurations of dielectric materials under different geometries. The implementation of a fuzzy neural network witch inputs these images for both quantitative and qualitative sensing is demonstrated. The architecture of the system is presented with some references to the general theory of fuzzy sets and fuzzy calculus. Experimental results are presented in the case of a set of 8 well characterized dielectric layers. Finally the effect of network parameters to the functionality of the system is discussed, especially in the case of functions evaluating the fuzzy AND and OR operations.

Associating Human-Centered Concepts with Social Networks Using Fuzzy Sets

NASA Astrophysics Data System (ADS)

Yager, Ronald R.

that allows us to determine how true it is that a particular node is a leader. In this work we look at the use of fuzzy set methodologies [8-10] to provide a bridge between the human analyst and the formal model of the network.

Differentially Coexpressed Disease Gene Identification Based on Gene Coexpression Network.

PubMed

Jiang, Xue; Zhang, Han; Quan, Xiongwen

2016-01-01

Screening disease-related genes by analyzing gene expression data has become a popular theme. Traditional disease-related gene selection methods always focus on identifying differentially expressed gene between case samples and a control group. These traditional methods may not fully consider the changes of interactions between genes at different cell states and the dynamic processes of gene expression levels during the disease progression. However, in order to understand the mechanism of disease, it is important to explore the dynamic changes of interactions between genes in biological networks at different cell states. In this study, we designed a novel framework to identify disease-related genes and developed a differentially coexpressed disease-related gene identification method based on gene coexpression network (DCGN) to screen differentially coexpressed genes. We firstly constructed phase-specific gene coexpression network using time-series gene expression data and defined the conception of differential coexpression of genes in coexpression network. Then, we designed two metrics to measure the value of gene differential coexpression according to the change of local topological structures between different phase-specific networks. Finally, we conducted meta-analysis of gene differential coexpression based on the rank-product method. Experimental results demonstrated the feasibility and effectiveness of DCGN and the superior performance of DCGN over other popular disease-related gene selection methods through real-world gene expression data sets.

Fuzzy Adaptive Control for Intelligent Autonomous Space Exploration Problems

NASA Technical Reports Server (NTRS)

Esogbue, Augustine O.

1998-01-01

The principal objective of the research reported here is the re-design, analysis and optimization of our newly developed neural network fuzzy adaptive controller model for complex processes capable of learning fuzzy control rules using process data and improving its control through on-line adaption. The learned improvement is according to a performance objective function that provides evaluative feedback; this performance objective is broadly defined to meet long-range goals over time. Although fuzzy control had proven effective for complex, nonlinear, imprecisely-defined processes for which standard models and controls are either inefficient, impractical or cannot be derived, the state of the art prior to our work showed that procedures for deriving fuzzy control, however, were mostly ad hoc heuristics. The learning ability of neural networks was exploited to systematically derive fuzzy control and permit on-line adaption and in the process optimize control. The operation of neural networks integrates very naturally with fuzzy logic. The neural networks which were designed and tested using simulation software and simulated data, followed by realistic industrial data were reconfigured for application on several platforms as well as for the employment of improved algorithms. The statistical procedures of the learning process were investigated and evaluated with standard statistical procedures (such as ANOVA, graphical analysis of residuals, etc.). The computational advantage of dynamic programming-like methods of optimal control was used to permit on-line fuzzy adaptive control. Tests for the consistency, completeness and interaction of the control rules were applied. Comparisons to other methods and controllers were made so as to identify the major advantages of the resulting controller model. Several specific modifications and extensions were made to the original controller. Additional modifications and explorations have been proposed for further study. Some of

A hierarchical two-phase framework for selecting genes in cancer datasets with a neuro-fuzzy system.

PubMed

Lim, Jongwoo; Wang, Bohyun; Lim, Joon S

2016-04-29

Finding the minimum number of appropriate biomarkers for specific targets such as a lung cancer has been a challenging issue in bioinformatics. We propose a hierarchical two-phase framework for selecting appropriate biomarkers that extracts candidate biomarkers from the cancer microarray datasets and then selects the minimum number of appropriate biomarkers from the extracted candidate biomarkers datasets with a specific neuro-fuzzy algorithm, which is called a neural network with weighted fuzzy membership function (NEWFM). In this context, as the first phase, the proposed framework is to extract candidate biomarkers by using a Bhattacharyya distance method that measures the similarity of two discrete probability distributions. Finally, the proposed framework is able to reduce the cost of finding biomarkers by not receiving medical supplements and improve the accuracy of the biomarkers in specific cancer target datasets.

A fuzzy Bayesian network approach to quantify the human behaviour during an evacuation

NASA Astrophysics Data System (ADS)

Ramli, Nurulhuda; Ghani, Noraida Abdul; Ahmad, Nazihah

2016-06-01

Bayesian Network (BN) has been regarded as a successful representation of inter-relationship of factors affecting human behavior during an emergency. This paper is an extension of earlier work of quantifying the variables involved in the BN model of human behavior during an evacuation using a well-known direct probability elicitation technique. To overcome judgment bias and reduce the expert's burden in providing precise probability values, a new approach for the elicitation technique is required. This study proposes a new fuzzy BN approach for quantifying human behavior during an evacuation. Three major phases of methodology are involved, namely 1) development of qualitative model representing human factors during an evacuation, 2) quantification of BN model using fuzzy probability and 3) inferencing and interpreting the BN result. A case study of three inter-dependencies of human evacuation factors such as danger assessment ability, information about the threat and stressful conditions are used to illustrate the application of the proposed method. This approach will serve as an alternative to the conventional probability elicitation technique in understanding the human behavior during an evacuation.

Abrasive slurry jet cutting model based on fuzzy relations

NASA Astrophysics Data System (ADS)

Qiang, C. H.; Guo, C. W.

2017-12-01

The cutting process of pre-mixed abrasive slurry or suspension jet (ASJ) is a complex process affected by many factors, and there is a highly nonlinear relationship between the cutting parameters and cutting quality. In this paper, guided by fuzzy theory, the fuzzy cutting model of ASJ was developed. In the modeling of surface roughness, the upper surface roughness prediction model and the lower surface roughness prediction model were established respectively. The adaptive fuzzy inference system combines the learning mechanism of neural networks and the linguistic reasoning ability of the fuzzy system, membership functions, and fuzzy rules are obtained by adaptive adjustment. Therefore, the modeling process is fast and effective. In this paper, the ANFIS module of MATLAB fuzzy logic toolbox was used to establish the fuzzy cutting model of ASJ, which is found to be quite instrumental to ASJ cutting applications.

Genetic networks and soft computing.

PubMed

Mitra, Sushmita; Das, Ranajit; Hayashi, Yoichi

2011-01-01

The analysis of gene regulatory networks provides enormous information on various fundamental cellular processes involving growth, development, hormone secretion, and cellular communication. Their extraction from available gene expression profiles is a challenging problem. Such reverse engineering of genetic networks offers insight into cellular activity toward prediction of adverse effects of new drugs or possible identification of new drug targets. Tasks such as classification, clustering, and feature selection enable efficient mining of knowledge about gene interactions in the form of networks. It is known that biological data is prone to different kinds of noise and ambiguity. Soft computing tools, such as fuzzy sets, evolutionary strategies, and neurocomputing, have been found to be helpful in providing low-cost, acceptable solutions in the presence of various types of uncertainties. In this paper, we survey the role of these soft methodologies and their hybridizations, for the purpose of generating genetic networks.

Differentiating malignant from benign breast tumors on acoustic radiation force impulse imaging using fuzzy-based neural networks with principle component analysis

NASA Astrophysics Data System (ADS)

Liu, Hsiao-Chuan; Chou, Yi-Hong; Tiu, Chui-Mei; Hsieh, Chi-Wen; Liu, Brent; Shung, K. Kirk

2017-03-01

Many modalities have been developed as screening tools for breast cancer. A new screening method called acoustic radiation force impulse (ARFI) imaging was created for distinguishing breast lesions based on localized tissue displacement. This displacement was quantitated by virtual touch tissue imaging (VTI). However, VTIs sometimes express reverse results to intensity information in clinical observation. In the study, a fuzzy-based neural network with principle component analysis (PCA) was proposed to differentiate texture patterns of malignant breast from benign tumors. Eighty VTIs were randomly retrospected. Thirty four patients were determined as BI-RADS category 2 or 3, and the rest of them were determined as BI-RADS category 4 or 5 by two leading radiologists. Morphological method and Boolean algebra were performed as the image preprocessing to acquire region of interests (ROIs) on VTIs. Twenty four quantitative parameters deriving from first-order statistics (FOS), fractal dimension and gray level co-occurrence matrix (GLCM) were utilized to analyze the texture pattern of breast tumors on VTIs. PCA was employed to reduce the dimension of features. Fuzzy-based neural network as a classifier to differentiate malignant from benign breast tumors. Independent samples test was used to examine the significance of the difference between benign and malignant breast tumors. The area Az under the receiver operator characteristic (ROC) curve, sensitivity, specificity and accuracy were calculated to evaluate the performance of the system. Most all of texture parameters present significant difference between malignant and benign tumors with p-value of less than 0.05 except the average of fractal dimension. For all features classified by fuzzy-based neural network, the sensitivity, specificity, accuracy and Az were 95.7%, 97.1%, 95% and 0.964, respectively. However, the sensitivity, specificity, accuracy and Az can be increased to 100%, 97.1%, 98.8% and 0.985, respectively

Mamdani Fuzzy System for Indoor Autonomous Mobile Robot

NASA Astrophysics Data System (ADS)

Khan, M. K. A. Ahamed; Rashid, Razif; Elamvazuthi, I.

2011-06-01

Several control algorithms for autonomous mobile robot navigation have been proposed in the literature. Recently, the employment of non-analytical methods of computing such as fuzzy logic, evolutionary computation, and neural networks has demonstrated the utility and potential of these paradigms for intelligent control of mobile robot navigation. In this paper, Mamdani fuzzy system for an autonomous mobile robot is developed. The paper begins with the discussion on the conventional controller and then followed by the description of fuzzy logic controller in detail.

Boosted ARTMAP: modifications to fuzzy ARTMAP motivated by boosting theory.

PubMed

Verzi, Stephen J; Heileman, Gregory L; Georgiopoulos, Michael

2006-05-01

In this paper, several modifications to the Fuzzy ARTMAP neural network architecture are proposed for conducting classification in complex, possibly noisy, environments. The goal of these modifications is to improve upon the generalization performance of Fuzzy ART-based neural networks, such as Fuzzy ARTMAP, in these situations. One of the major difficulties of employing Fuzzy ARTMAP on such learning problems involves over-fitting of the training data. Structural risk minimization is a machine-learning framework that addresses the issue of over-fitting by providing a backbone for analysis as well as an impetus for the design of better learning algorithms. The theory of structural risk minimization reveals a trade-off between training error and classifier complexity in reducing generalization error, which will be exploited in the learning algorithms proposed in this paper. Boosted ART extends Fuzzy ART by allowing the spatial extent of each cluster formed to be adjusted independently. Boosted ARTMAP generalizes upon Fuzzy ARTMAP by allowing non-zero training error in an effort to reduce the hypothesis complexity and hence improve overall generalization performance. Although Boosted ARTMAP is strictly speaking not a boosting algorithm, the changes it encompasses were motivated by the goals that one strives to achieve when employing boosting. Boosted ARTMAP is an on-line learner, it does not require excessive parameter tuning to operate, and it reduces precisely to Fuzzy ARTMAP for particular parameter values. Another architecture described in this paper is Structural Boosted ARTMAP, which uses both Boosted ART and Boosted ARTMAP to perform structural risk minimization learning. Structural Boosted ARTMAP will allow comparison of the capabilities of off-line versus on-line learning as well as empirical risk minimization versus structural risk minimization using Fuzzy ARTMAP-based neural network architectures. Both empirical and theoretical results are presented to

A medical cost estimation with fuzzy neural network of acute hepatitis patients in emergency room.

PubMed

Kuo, R J; Cheng, W C; Lien, W C; Yang, T J

2015-10-01

Taiwan is an area where chronic hepatitis is endemic. Liver cancer is so common that it has been ranked first among cancer mortality rates since the early 1980s in Taiwan. Besides, liver cirrhosis and chronic liver diseases are the sixth or seventh in the causes of death. Therefore, as shown by the active research on hepatitis, it is not only a health threat, but also a huge medical cost for the government. The estimated total number of hepatitis B carriers in the general population aged more than 20 years old is 3,067,307. Thus, a case record review was conducted from all patients with diagnosis of acute hepatitis admitted to the Emergency Department (ED) of a well-known teaching-oriented hospital in Taipei. The cost of medical resource utilization is defined as the total medical fee. In this study, a fuzzy neural network is employed to develop the cost forecasting model. A total of 110 patients met the inclusion criteria. The computational results indicate that the FNN model can provide more accurate forecasts than the support vector regression (SVR) or artificial neural network (ANN). In addition, unlike SVR and ANN, FNN can also provide fuzzy IF-THEN rules for interpretation. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Analysis of bHLH coding genes using gene co-expression network approach.

PubMed

Srivastava, Swati; Sanchita; Singh, Garima; Singh, Noopur; Srivastava, Gaurava; Sharma, Ashok

2016-07-01

Network analysis provides a powerful framework for the interpretation of data. It uses novel reference network-based metrices for module evolution. These could be used to identify module of highly connected genes showing variation in co-expression network. In this study, a co-expression network-based approach was used for analyzing the genes from microarray data. Our approach consists of a simple but robust rank-based network construction. The publicly available gene expression data of Solanum tuberosum under cold and heat stresses were considered to create and analyze a gene co-expression network. The analysis provide highly co-expressed module of bHLH coding genes based on correlation values. Our approach was to analyze the variation of genes expression, according to the time period of stress through co-expression network approach. As the result, the seed genes were identified showing multiple connections with other genes in the same cluster. Seed genes were found to be vary in different time periods of stress. These analyzed seed genes may be utilized further as marker genes for developing the stress tolerant plant species.

Fuzzy/Neural Software Estimates Costs of Rocket-Engine Tests

NASA Technical Reports Server (NTRS)

Douglas, Freddie; Bourgeois, Edit Kaminsky

2005-01-01

The Highly Accurate Cost Estimating Model (HACEM) is a software system for estimating the costs of testing rocket engines and components at Stennis Space Center. HACEM is built on a foundation of adaptive-network-based fuzzy inference systems (ANFIS) a hybrid software concept that combines the adaptive capabilities of neural networks with the ease of development and additional benefits of fuzzy-logic-based systems. In ANFIS, fuzzy inference systems are trained by use of neural networks. HACEM includes selectable subsystems that utilize various numbers and types of inputs, various numbers of fuzzy membership functions, and various input-preprocessing techniques. The inputs to HACEM are parameters of specific tests or series of tests. These parameters include test type (component or engine test), number and duration of tests, and thrust level(s) (in the case of engine tests). The ANFIS in HACEM are trained by use of sets of these parameters, along with costs of past tests. Thereafter, the user feeds HACEM a simple input text file that contains the parameters of a planned test or series of tests, the user selects the desired HACEM subsystem, and the subsystem processes the parameters into an estimate of cost(s).

A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain

NASA Technical Reports Server (NTRS)

Hall, Lawrence O.; Bensaid, Amine M.; Clarke, Laurence P.; Velthuizen, Robert P.; Silbiger, Martin S.; Bezdek, James C.

1992-01-01

Magnetic resonance (MR) brain section images are segmented and then synthetically colored to give visual representations of the original data with three approaches: the literal and approximate fuzzy c-means unsupervised clustering algorithms and a supervised computational neural network, a dynamic multilayered perception trained with the cascade correlation learning algorithm. Initial clinical results are presented on both normal volunteers and selected patients with brain tumors surrounded by edema. Supervised and unsupervised segmentation techniques provide broadly similar results. Unsupervised fuzzy algorithms were visually observed to show better segmentation when compared with raw image data for volunteer studies. However, for a more complex segmentation problem with tumor/edema or cerebrospinal fluid boundary, where the tissues have similar MR relaxation behavior, inconsistency in rating among experts was observed.

Fuzzy Logic-based Intelligent Scheme for Enhancing QoS of Vertical Handover Decision in Vehicular Ad-hoc Networks

NASA Astrophysics Data System (ADS)

Azzali, F.; Ghazali, O.; Omar, M. H.

2017-08-01

The design of next generation networks in various technologies under the “Anywhere, Anytime” paradigm offers seamless connectivity across different coverage. A conventional algorithm such as RSSThreshold algorithm, that only uses the received strength signal (RSS) as a metric, will decrease handover performance regarding handover latency, delay, packet loss, and handover failure probability. Moreover, the RSS-based algorithm is only suitable for horizontal handover decision to examine the quality of service (QoS) compared to the vertical handover decision in advanced technologies. In the next generation network, vertical handover can be started based on the user’s convenience or choice rather than connectivity reasons. This study proposes a vertical handover decision algorithm that uses a Fuzzy Logic (FL) algorithm, to increase QoS performance in heterogeneous vehicular ad-hoc networks (VANET). The study uses network simulator 2.29 (NS 2.29) along with the mobility traffic network and generator to implement simulation scenarios and topologies. This helps the simulation to achieve a realistic VANET mobility scenario. The required analysis on the performance of QoS in the vertical handover can thus be conducted. The proposed Fuzzy Logic algorithm shows improvement over the conventional algorithm (RSSThreshold) in the average percentage of handover QoS whereby it achieves 20%, 21% and 13% improvement on handover latency, delay, and packet loss respectively. This is achieved through triggering a process in layer two and three that enhances the handover performance.

Review of Medical Image Classification using the Adaptive Neuro-Fuzzy Inference System

PubMed Central

Hosseini, Monireh Sheikh; Zekri, Maryam

2012-01-01

Image classification is an issue that utilizes image processing, pattern recognition and classification methods. Automatic medical image classification is a progressive area in image classification, and it is expected to be more developed in the future. Because of this fact, automatic diagnosis can assist pathologists by providing second opinions and reducing their workload. This paper reviews the application of the adaptive neuro-fuzzy inference system (ANFIS) as a classifier in medical image classification during the past 16 years. ANFIS is a fuzzy inference system (FIS) implemented in the framework of an adaptive fuzzy neural network. It combines the explicit knowledge representation of an FIS with the learning power of artificial neural networks. The objective of ANFIS is to integrate the best features of fuzzy systems and neural networks. A brief comparison with other classifiers, main advantages and drawbacks of this classifier are investigated. PMID:23493054

Information Warfare-Worthy Jamming Attack Detection Mechanism for Wireless Sensor Networks Using a Fuzzy Inference System

PubMed Central

Misra, Sudip; Singh, Ranjit; Rohith Mohan, S. V.

2010-01-01

The proposed mechanism for jamming attack detection for wireless sensor networks is novel in three respects: firstly, it upgrades the jammer to include versatile military jammers; secondly, it graduates from the existing node-centric detection system to the network-centric system making it robust and economical at the nodes, and thirdly, it tackles the problem through fuzzy inference system, as the decision regarding intensity of jamming is seldom crisp. The system with its high robustness, ability to grade nodes with jamming indices, and its true-detection rate as high as 99.8%, is worthy of consideration for information warfare defense purposes. PMID:22319307

Fuzzy bi-objective preventive health care network design.

PubMed

Davari, Soheil; Kilic, Kemal; Ertek, Gurdal

2015-09-01

Preventive health care is unlike health care for acute ailments, as people are less alert to their unknown medical problems. In order to motivate public and to attain desired participation levels for preventive programs, the attractiveness of the health care facility is a major concern. Health economics literature indicates that attractiveness of a facility is significantly influenced by proximity of the clients to it. Hence attractiveness is generally modelled as a function of distance. However, abundant empirical evidence suggests that other qualitative factors such as perceived quality, attractions nearby, amenities, etc. also influence attractiveness. Therefore, a realistic measure should incorporate the vagueness in the concept of attractiveness to the model. The public policy makers should also maintain the equity among various neighborhoods, which should be considered as a second objective. Finally, even though the general tendency in the literature is to focus on health benefits, the cost effectiveness is still a factor that should be considered. In this paper, a fuzzy bi-objective model with budget constraints is developed. Later, by modelling the attractiveness by means of fuzzy triangular numbers and treating the budget constraint as a soft constraint, a modified (and more realistic) version of the model is introduced. Two solution methodologies, namely fuzzy goal programming and fuzzy chance constrained optimization are proposed as solutions. Both the original and the modified models are solved within the framework of a case study in Istanbul, Turkey. In the case study, the Microsoft Bing Map is utilized in order to determine more accurate distance measures among the nodes.

Fuzzy-cellular neural network for face recognition HCI Authentication

NASA Astrophysics Data System (ADS)

Hoomod, Haider K.; ali, Ahmed abd

2018-05-01

Because of the rapid development of mobile devices technology, ease of use and interact with humans. May have found a mobile device most uses in our communications. Mobile devices can carry large amounts of personal and sensitive data, but often left not guaranteed (pin) locks are inconvenient to use and thus have seen low adoption while biometrics is more convenient and less susceptible to fraud and manipulation. Were propose in this paper authentication technique for using a mobile face recognition based on cellular neural networks [1] and fuzzy rules control. The good speed and get recognition rate from applied the proposed system in Android system. The images obtained in real time for 60 persons each person has 20 t0 60 different shot face images (about 3600 images), were the results for (FAR = 0), (FRR = 1.66%), (FER = 1.66) and accuracy = 98.34

Constructing an integrated gene similarity network for the identification of disease genes.

PubMed

Tian, Zhen; Guo, Maozu; Wang, Chunyu; Xing, LinLin; Wang, Lei; Zhang, Yin

2017-09-20

Discovering novel genes that are involved human diseases is a challenging task in biomedical research. In recent years, several computational approaches have been proposed to prioritize candidate disease genes. Most of these methods are mainly based on protein-protein interaction (PPI) networks. However, since these PPI networks contain false positives and only cover less half of known human genes, their reliability and coverage are very low. Therefore, it is highly necessary to fuse multiple genomic data to construct a credible gene similarity network and then infer disease genes on the whole genomic scale. We proposed a novel method, named RWRB, to infer causal genes of interested diseases. First, we construct five individual gene (protein) similarity networks based on multiple genomic data of human genes. Then, an integrated gene similarity network (IGSN) is reconstructed based on similarity network fusion (SNF) method. Finally, we employee the random walk with restart algorithm on the phenotype-gene bilayer network, which combines phenotype similarity network, IGSN as well as phenotype-gene association network, to prioritize candidate disease genes. We investigate the effectiveness of RWRB through leave-one-out cross-validation methods in inferring phenotype-gene relationships. Results show that RWRB is more accurate than state-of-the-art methods on most evaluation metrics. Further analysis shows that the success of RWRB is benefited from IGSN which has a wider coverage and higher reliability comparing with current PPI networks. Moreover, we conduct a comprehensive case study for Alzheimer's disease and predict some novel disease genes that supported by literature. RWRB is an effective and reliable algorithm in prioritizing candidate disease genes on the genomic scale. Software and supplementary information are available at http://nclab.hit.edu.cn/~tianzhen/RWRB/ .

Temperature based daily incoming solar radiation modeling based on gene expression programming, neuro-fuzzy and neural network computing techniques.

NASA Astrophysics Data System (ADS)

Landeras, G.; López, J. J.; Kisi, O.; Shiri, J.

2012-04-01

The correct observation/estimation of surface incoming solar radiation (RS) is very important for many agricultural, meteorological and hydrological related applications. While most weather stations are provided with sensors for air temperature detection, the presence of sensors necessary for the detection of solar radiation is not so habitual and the data quality provided by them is sometimes poor. In these cases it is necessary to estimate this variable. Temperature based modeling procedures are reported in this study for estimating daily incoming solar radiation by using Gene Expression Programming (GEP) for the first time, and other artificial intelligence models such as Artificial Neural Networks (ANNs), and Adaptive Neuro-Fuzzy Inference System (ANFIS). Traditional temperature based solar radiation equations were also included in this study and compared with artificial intelligence based approaches. Root mean square error (RMSE), mean absolute error (MAE) RMSE-based skill score (SSRMSE), MAE-based skill score (SSMAE) and r2 criterion of Nash and Sutcliffe criteria were used to assess the models' performances. An ANN (a four-input multilayer perceptron with ten neurons in the hidden layer) presented the best performance among the studied models (2.93 MJ m-2 d-1 of RMSE). A four-input ANFIS model revealed as an interesting alternative to ANNs (3.14 MJ m-2 d-1 of RMSE). Very limited number of studies has been done on estimation of solar radiation based on ANFIS, and the present one demonstrated the ability of ANFIS to model solar radiation based on temperatures and extraterrestrial radiation. By the way this study demonstrated, for the first time, the ability of GEP models to model solar radiation based on daily atmospheric variables. Despite the accuracy of GEP models was slightly lower than the ANFIS and ANN models the genetic programming models (i.e., GEP) are superior to other artificial intelligence models in giving a simple explicit equation for the

Therapeutic synthetic gene networks.

PubMed

Karlsson, Maria; Weber, Wilfried

2012-10-01

The field of synthetic biology is rapidly expanding and has over the past years evolved from the development of simple gene networks to complex treatment-oriented circuits. The reprogramming of cell fate with open-loop or closed-loop synthetic control circuits along with biologically implemented logical functions have fostered applications spanning over a wide range of disciplines, including artificial insemination, personalized medicine and the treatment of cancer and metabolic disorders. In this review we describe several applications of interactive gene networks, a synthetic biology-based approach for future gene therapy, as well as the utilization of synthetic gene circuits as blueprints for the design of stimuli-responsive biohybrid materials. The recent progress in synthetic biology, including the rewiring of biosensing devices with the body's endogenous network as well as novel therapeutic approaches originating from interdisciplinary work, generates numerous opportunities for future biomedical applications. Copyright © 2012 Elsevier Ltd. All rights reserved.

An Efficient Interval Type-2 Fuzzy CMAC for Chaos Time-Series Prediction and Synchronization.

PubMed

Lee, Ching-Hung; Chang, Feng-Yu; Lin, Chih-Min

2014-03-01

This paper aims to propose a more efficient control algorithm for chaos time-series prediction and synchronization. A novel type-2 fuzzy cerebellar model articulation controller (T2FCMAC) is proposed. In some special cases, this T2FCMAC can be reduced to an interval type-2 fuzzy neural network, a fuzzy neural network, and a fuzzy cerebellar model articulation controller (CMAC). So, this T2FCMAC is a more generalized network with better learning ability, thus, it is used for the chaos time-series prediction and synchronization. Moreover, this T2FCMAC realizes the un-normalized interval type-2 fuzzy logic system based on the structure of the CMAC. It can provide better capabilities for handling uncertainty and more design degree of freedom than traditional type-1 fuzzy CMAC. Unlike most of the interval type-2 fuzzy system, the type-reduction of T2FCMAC is bypassed due to the property of un-normalized interval type-2 fuzzy logic system. This causes T2FCMAC to have lower computational complexity and is more practical. For chaos time-series prediction and synchronization applications, the training architectures with corresponding convergence analyses and optimal learning rates based on Lyapunov stability approach are introduced. Finally, two illustrated examples are presented to demonstrate the performance of the proposed T2FCMAC.

System-level insights into the cellular interactome of a non-model organism: inferring, modelling and analysing functional gene network of soybean (Glycine max).

PubMed

Xu, Yungang; Guo, Maozu; Zou, Quan; Liu, Xiaoyan; Wang, Chunyu; Liu, Yang

2014-01-01

Cellular interactome, in which genes and/or their products interact on several levels, forming transcriptional regulatory-, protein interaction-, metabolic-, signal transduction networks, etc., has attracted decades of research focuses. However, such a specific type of network alone can hardly explain the various interactive activities among genes. These networks characterize different interaction relationships, implying their unique intrinsic properties and defects, and covering different slices of biological information. Functional gene network (FGN), a consolidated interaction network that models fuzzy and more generalized notion of gene-gene relations, have been proposed to combine heterogeneous networks with the goal of identifying functional modules supported by multiple interaction types. There are yet no successful precedents of FGNs on sparsely studied non-model organisms, such as soybean (Glycine max), due to the absence of sufficient heterogeneous interaction data. We present an alternative solution for inferring the FGNs of soybean (SoyFGNs), in a pioneering study on the soybean interactome, which is also applicable to other organisms. SoyFGNs exhibit the typical characteristics of biological networks: scale-free, small-world architecture and modularization. Verified by co-expression and KEGG pathways, SoyFGNs are more extensive and accurate than an orthology network derived from Arabidopsis. As a case study, network-guided disease-resistance gene discovery indicates that SoyFGNs can provide system-level studies on gene functions and interactions. This work suggests that inferring and modelling the interactome of a non-model plant are feasible. It will speed up the discovery and definition of the functions and interactions of other genes that control important functions, such as nitrogen fixation and protein or lipid synthesis. The efforts of the study are the basis of our further comprehensive studies on the soybean functional interactome at the genome

System-Level Insights into the Cellular Interactome of a Non-Model Organism: Inferring, Modelling and Analysing Functional Gene Network of Soybean (Glycine max)

PubMed Central

Xu, Yungang; Guo, Maozu; Zou, Quan; Liu, Xiaoyan; Wang, Chunyu; Liu, Yang

2014-01-01

Cellular interactome, in which genes and/or their products interact on several levels, forming transcriptional regulatory-, protein interaction-, metabolic-, signal transduction networks, etc., has attracted decades of research focuses. However, such a specific type of network alone can hardly explain the various interactive activities among genes. These networks characterize different interaction relationships, implying their unique intrinsic properties and defects, and covering different slices of biological information. Functional gene network (FGN), a consolidated interaction network that models fuzzy and more generalized notion of gene-gene relations, have been proposed to combine heterogeneous networks with the goal of identifying functional modules supported by multiple interaction types. There are yet no successful precedents of FGNs on sparsely studied non-model organisms, such as soybean (Glycine max), due to the absence of sufficient heterogeneous interaction data. We present an alternative solution for inferring the FGNs of soybean (SoyFGNs), in a pioneering study on the soybean interactome, which is also applicable to other organisms. SoyFGNs exhibit the typical characteristics of biological networks: scale-free, small-world architecture and modularization. Verified by co-expression and KEGG pathways, SoyFGNs are more extensive and accurate than an orthology network derived from Arabidopsis. As a case study, network-guided disease-resistance gene discovery indicates that SoyFGNs can provide system-level studies on gene functions and interactions. This work suggests that inferring and modelling the interactome of a non-model plant are feasible. It will speed up the discovery and definition of the functions and interactions of other genes that control important functions, such as nitrogen fixation and protein or lipid synthesis. The efforts of the study are the basis of our further comprehensive studies on the soybean functional interactome at the genome

Distributed collaborative probabilistic design of multi-failure structure with fluid-structure interaction using fuzzy neural network of regression

NASA Astrophysics Data System (ADS)

Song, Lu-Kai; Wen, Jie; Fei, Cheng-Wei; Bai, Guang-Chen

2018-05-01

To improve the computing efficiency and precision of probabilistic design for multi-failure structure, a distributed collaborative probabilistic design method-based fuzzy neural network of regression (FR) (called as DCFRM) is proposed with the integration of distributed collaborative response surface method and fuzzy neural network regression model. The mathematical model of DCFRM is established and the probabilistic design idea with DCFRM is introduced. The probabilistic analysis of turbine blisk involving multi-failure modes (deformation failure, stress failure and strain failure) was investigated by considering fluid-structure interaction with the proposed method. The distribution characteristics, reliability degree, and sensitivity degree of each failure mode and overall failure mode on turbine blisk are obtained, which provides a useful reference for improving the performance and reliability of aeroengine. Through the comparison of methods shows that the DCFRM reshapes the probability of probabilistic analysis for multi-failure structure and improves the computing efficiency while keeping acceptable computational precision. Moreover, the proposed method offers a useful insight for reliability-based design optimization of multi-failure structure and thereby also enriches the theory and method of mechanical reliability design.

Creating Clinical Fuzzy Automata with Fuzzy Arden Syntax.

PubMed

de Bruin, Jeroen S; Steltzer, Heinz; Rappelsberger, Andrea; Adlassnig, Klaus-Peter

2017-01-01

Formal constructs for fuzzy sets and fuzzy logic are incorporated into Arden Syntax version 2.9 (Fuzzy Arden Syntax). With fuzzy sets, the relationships between measured or observed data and linguistic terms are expressed as degrees of compatibility that model the unsharpness of the boundaries of linguistic terms. Propositional uncertainty due to incomplete knowledge of relationships between clinical linguistic concepts is modeled with fuzzy logic. Fuzzy Arden Syntax also supports the construction of fuzzy state monitors. The latter are defined as monitors that employ fuzzy automata to observe gradual transitions between different stages of disease. As a use case, we re-implemented FuzzyARDS, a previously published clinical monitoring system for patients suffering from acute respiratory distress syndrome (ARDS). Using the re-implementation as an example, we show how key concepts of fuzzy automata, i.e., fuzzy states and parallel fuzzy state transitions, can be implemented in Fuzzy Arden Syntax. The results showed that fuzzy state monitors can be implemented in a straightforward manner.

The transfer and transformation of collective network information in gene-matched networks.

PubMed

Kitsukawa, Takashi; Yagi, Takeshi

2015-10-09

Networks, such as the human society network, social and professional networks, and biological system networks, contain vast amounts of information. Information signals in networks are distributed over nodes and transmitted through intricately wired links, making the transfer and transformation of such information difficult to follow. Here we introduce a novel method for describing network information and its transfer using a model network, the Gene-matched network (GMN), in which nodes (neurons) possess attributes (genes). In the GMN, nodes are connected according to their expression of common genes. Because neurons have multiple genes, the GMN is cluster-rich. We show that, in the GMN, information transfer and transformation were controlled systematically, according to the activity level of the network. Furthermore, information transfer and transformation could be traced numerically with a vector using genes expressed in the activated neurons, the active-gene array, which was used to assess the relative activity among overlapping neuronal groups. Interestingly, this coding style closely resembles the cell-assembly neural coding theory. The method introduced here could be applied to many real-world networks, since many systems, including human society and various biological systems, can be represented as a network of this type.

Adaptive critic autopilot design of bank-to-turn missiles using fuzzy basis function networks.

PubMed

Lin, Chuan-Kai

2005-04-01

A new adaptive critic autopilot design for bank-to-turn missiles is presented. In this paper, the architecture of adaptive critic learning scheme contains a fuzzy-basis-function-network based associative search element (ASE), which is employed to approximate nonlinear and complex functions of bank-to-turn missiles, and an adaptive critic element (ACE) generating the reinforcement signal to tune the associative search element. In the design of the adaptive critic autopilot, the control law receives signals from a fixed gain controller, an ASE and an adaptive robust element, which can eliminate approximation errors and disturbances. Traditional adaptive critic reinforcement learning is the problem faced by an agent that must learn behavior through trial-and-error interactions with a dynamic environment, however, the proposed tuning algorithm can significantly shorten the learning time by online tuning all parameters of fuzzy basis functions and weights of ASE and ACE. Moreover, the weight updating law derived from the Lyapunov stability theory is capable of guaranteeing both tracking performance and stability. Computer simulation results confirm the effectiveness of the proposed adaptive critic autopilot.

Robust stochastic stability of discrete-time fuzzy Markovian jump neural networks.

PubMed

Arunkumar, A; Sakthivel, R; Mathiyalagan, K; Park, Ju H

2014-07-01

This paper focuses the issue of robust stochastic stability for a class of uncertain fuzzy Markovian jumping discrete-time neural networks (FMJDNNs) with various activation functions and mixed time delay. By employing the Lyapunov technique and linear matrix inequality (LMI) approach, a new set of delay-dependent sufficient conditions are established for the robust stochastic stability of uncertain FMJDNNs. More precisely, the parameter uncertainties are assumed to be time varying, unknown and norm bounded. The obtained stability conditions are established in terms of LMIs, which can be easily checked by using the efficient MATLAB-LMI toolbox. Finally, numerical examples with simulation result are provided to illustrate the effectiveness and less conservativeness of the obtained results. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

An intelligent load shedding scheme using neural networks and neuro-fuzzy.

PubMed

Haidar, Ahmed M A; Mohamed, Azah; Al-Dabbagh, Majid; Hussain, Aini; Masoum, Mohammad

2009-12-01

Load shedding is some of the essential requirement for maintaining security of modern power systems, particularly in competitive energy markets. This paper proposes an intelligent scheme for fast and accurate load shedding using neural networks for predicting the possible loss of load at the early stage and neuro-fuzzy for determining the amount of load shed in order to avoid a cascading outage. A large scale electrical power system has been considered to validate the performance of the proposed technique in determining the amount of load shed. The proposed techniques can provide tools for improving the reliability and continuity of power supply. This was confirmed by the results obtained in this research of which sample results are given in this paper.

Distributed k-Means Algorithm and Fuzzy c-Means Algorithm for Sensor Networks Based on Multiagent Consensus Theory.

PubMed

Qin, Jiahu; Fu, Weiming; Gao, Huijun; Zheng, Wei Xing

2016-03-03

This paper is concerned with developing a distributed k-means algorithm and a distributed fuzzy c-means algorithm for wireless sensor networks (WSNs) where each node is equipped with sensors. The underlying topology of the WSN is supposed to be strongly connected. The consensus algorithm in multiagent consensus theory is utilized to exchange the measurement information of the sensors in WSN. To obtain a faster convergence speed as well as a higher possibility of having the global optimum, a distributed k-means++ algorithm is first proposed to find the initial centroids before executing the distributed k-means algorithm and the distributed fuzzy c-means algorithm. The proposed distributed k-means algorithm is capable of partitioning the data observed by the nodes into measure-dependent groups which have small in-group and large out-group distances, while the proposed distributed fuzzy c-means algorithm is capable of partitioning the data observed by the nodes into different measure-dependent groups with degrees of membership values ranging from 0 to 1. Simulation results show that the proposed distributed algorithms can achieve almost the same results as that given by the centralized clustering algorithms.

Fuzzy Comprehensive Evaluation (FCE) in Military Decision Support Processes

DTIC Science & Technology

2013-12-01

detection in military aircraft (Brotherton & Johnson, 2001). Today, as reported by the international journal Advances in Fuzzy Systems (2013), as of March... detection for advanced military aircraft using neural networks. In IEEE Proceedings Aerospace Conference, 2001, 6, 3113–3123. Cheng, C . H. (1996... C .   OBJECTIVE ................................................................................................... 3   II.   THE FUZZY

Probabilistic representation of gene regulatory networks.

PubMed

Mao, Linyong; Resat, Haluk

2004-09-22

Recent experiments have established unambiguously that biological systems can have significant cell-to-cell variations in gene expression levels even in isogenic populations. Computational approaches to studying gene expression in cellular systems should capture such biological variations for a more realistic representation. In this paper, we present a new fully probabilistic approach to the modeling of gene regulatory networks that allows for fluctuations in the gene expression levels. The new algorithm uses a very simple representation for the genes, and accounts for the repression or induction of the genes and for the biological variations among isogenic populations simultaneously. Because of its simplicity, introduced algorithm is a very promising approach to model large-scale gene regulatory networks. We have tested the new algorithm on the synthetic gene network library bioengineered recently. The good agreement between the computed and the experimental results for this library of networks, and additional tests, demonstrate that the new algorithm is robust and very successful in explaining the experimental data. The simulation software is available upon request. Supplementary material will be made available on the OUP server.

North American Fuzzy Logic Processing Society (NAFIPS 1992), volume 1

NASA Technical Reports Server (NTRS)

Villarreal, James A. (Compiler)

1992-01-01

This document contains papers presented at the NAFIPS '92 North American Fuzzy Information Processing Society Conference. More than 75 papers were presented at this Conference, which was sponsored by NAFIPS in cooperation with NASA, the Instituto Tecnologico de Morelia, the Indian Society for Fuzzy Mathematics and Information Processing (ISFUMIP), the Instituto Tecnologico de Estudios Superiores de Monterrey (ITESM), the International Fuzzy Systems Association (IFSA), the Japan Society for Fuzzy Theory and Systems, and the Microelectronics and Computer Technology Corporation (MCC). The fuzzy set theory has led to a large number of diverse applications. Recently, interesting applications have been developed which involve the integration of fuzzy systems with adaptive processes such as neural networks and genetic algorithms. NAFIPS '92 was directed toward the advancement, commercialization, and engineering development of these technologies.

North American Fuzzy Logic Processing Society (NAFIPS 1992), volume 2

NASA Technical Reports Server (NTRS)

Villarreal, James A. (Compiler)

1992-01-01

This document contains papers presented at the NAFIPS '92 North American Fuzzy Information Processing Society Conference. More than 75 papers were presented at this Conference, which was sponsored by NAFIPS in cooperation with NASA, the Instituto Tecnologico de Morelia, the Indian Society for Fuzzy Mathematics and Information Processing (ISFUMIP), the Instituto Tecnologico de Estudios Superiores de Monterrey (ITESM), the International Fuzzy Systems Association (IFSA), the Japan Society for Fuzzy Theory and Systems, and the Microelectronics and Computer Technology Corporation (MCC). The fuzzy set theory has led to a large number of diverse applications. Recently, interesting applications have been developed which involve the integration of fuzzy systems with adaptive processes such a neural networks and genetic algorithms. NAFIPS '92 was directed toward the advancement, commercialization, and engineering development of these technologies.

Estimation of dew point temperature using neuro-fuzzy and neural network techniques

NASA Astrophysics Data System (ADS)

Kisi, Ozgur; Kim, Sungwon; Shiri, Jalal

2013-11-01

This study investigates the ability of two different artificial neural network (ANN) models, generalized regression neural networks model (GRNNM) and Kohonen self-organizing feature maps neural networks model (KSOFM), and two different adaptive neural fuzzy inference system (ANFIS) models, ANFIS model with sub-clustering identification (ANFIS-SC) and ANFIS model with grid partitioning identification (ANFIS-GP), for estimating daily dew point temperature. The climatic data that consisted of 8 years of daily records of air temperature, sunshine hours, wind speed, saturation vapor pressure, relative humidity, and dew point temperature from three weather stations, Daego, Pohang, and Ulsan, in South Korea were used in the study. The estimates of ANN and ANFIS models were compared according to the three different statistics, root mean square errors, mean absolute errors, and determination coefficient. Comparison results revealed that the ANFIS-SC, ANFIS-GP, and GRNNM models showed almost the same accuracy and they performed better than the KSOFM model. Results also indicated that the sunshine hours, wind speed, and saturation vapor pressure have little effect on dew point temperature. It was found that the dew point temperature could be successfully estimated by using T mean and R H variables.

Self-learning fuzzy controllers based on temporal back propagation

NASA Technical Reports Server (NTRS)

Jang, Jyh-Shing R.

1992-01-01

This paper presents a generalized control strategy that enhances fuzzy controllers with self-learning capability for achieving prescribed control objectives in a near-optimal manner. This methodology, termed temporal back propagation, is model-insensitive in the sense that it can deal with plants that can be represented in a piecewise-differentiable format, such as difference equations, neural networks, GMDH structures, and fuzzy models. Regardless of the numbers of inputs and outputs of the plants under consideration, the proposed approach can either refine the fuzzy if-then rules if human experts, or automatically derive the fuzzy if-then rules obtained from human experts are not available. The inverted pendulum system is employed as a test-bed to demonstrate the effectiveness of the proposed control scheme and the robustness of the acquired fuzzy controller.

A novel multi-model neuro-fuzzy-based MPPT for three-phase grid-connected photovoltaic system

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chaouachi, Aymen; Kamel, Rashad M.; Nagasaka, Ken

This paper presents a novel methodology for Maximum Power Point Tracking (MPPT) of a grid-connected 20 kW photovoltaic (PV) system using neuro-fuzzy network. The proposed method predicts the reference PV voltage guarantying optimal power transfer between the PV generator and the main utility grid. The neuro-fuzzy network is composed of a fuzzy rule-based classifier and three multi-layered feed forwarded Artificial Neural Networks (ANN). Inputs of the network (irradiance and temperature) are classified before they are fed into the appropriated ANN for either training or estimation process while the output is the reference voltage. The main advantage of the proposed methodology,more » comparing to a conventional single neural network-based approach, is the distinct generalization ability regarding to the nonlinear and dynamic behavior of a PV generator. In fact, the neuro-fuzzy network is a neural network based multi-model machine learning that defines a set of local models emulating the complex and nonlinear behavior of a PV generator under a wide range of operating conditions. Simulation results under several rapid irradiance variations proved that the proposed MPPT method fulfilled the highest efficiency comparing to a conventional single neural network and the Perturb and Observe (P and O) algorithm dispositive. (author)« less

Trends and Issues in Fuzzy Control and Neuro-Fuzzy Modeling

NASA Technical Reports Server (NTRS)

Chiu, Stephen

1996-01-01

Everyday experience in building and repairing things around the home have taught us the importance of using the right tool for the right job. Although we tend to think of a 'job' in broad terms, such as 'build a bookcase,' we understand well that the 'right job' associated with each 'right tool' is typically a narrowly bounded subtask, such as 'tighten the screws.' Unfortunately, we often lose sight of this principle when solving engineering problems; we treat a broadly defined problem, such as controlling or modeling a system, as a narrow one that has a single 'right tool' (e.g., linear analysis, fuzzy logic, neural network). We need to recognize that a typical real-world problem contains a number of different sub-problems, and that a truly optimal solution (the best combination of cost, performance and feature) is obtained by applying the right tool to the right sub-problem. Here I share some of my perspectives on what constitutes the 'right job' for fuzzy control and describe recent advances in neuro-fuzzy modeling to illustrate and to motivate the synergistic use of different tools.

Construction and analysis of gene-gene dynamics influence networks based on a Boolean model.

PubMed

Mazaya, Maulida; Trinh, Hung-Cuong; Kwon, Yung-Keun

2017-12-21

Identification of novel gene-gene relations is a crucial issue to understand system-level biological phenomena. To this end, many methods based on a correlation analysis of gene expressions or structural analysis of molecular interaction networks have been proposed. They have a limitation in identifying more complicated gene-gene dynamical relations, though. To overcome this limitation, we proposed a measure to quantify a gene-gene dynamical influence (GDI) using a Boolean network model and constructed a GDI network to indicate existence of a dynamical influence for every ordered pair of genes. It represents how much a state trajectory of a target gene is changed by a knockout mutation subject to a source gene in a gene-gene molecular interaction (GMI) network. Through a topological comparison between GDI and GMI networks, we observed that the former network is denser than the latter network, which implies that there exist many gene pairs of dynamically influencing but molecularly non-interacting relations. In addition, a larger number of hub genes were generated in the GDI network. On the other hand, there was a correlation between these networks such that the degree value of a node was positively correlated to each other. We further investigated the relationships of the GDI value with structural properties and found that there are negative and positive correlations with the length of a shortest path and the number of paths, respectively. In addition, a GDI network could predict a set of genes whose steady-state expression is affected in E. coli gene-knockout experiments. More interestingly, we found that the drug-targets with side-effects have a larger number of outgoing links than the other genes in the GDI network, which implies that they are more likely to influence the dynamics of other genes. Finally, we found biological evidences showing that the gene pairs which are not molecularly interacting but dynamically influential can be considered for novel gene-gene

Multiple fuzzy neural network system for outcome prediction and classification of 220 lymphoma patients on the basis of molecular profiling.

PubMed

Ando, Tatsuya; Suguro, Miyuki; Kobayashi, Takeshi; Seto, Masao; Honda, Hiroyuki

2003-10-01

A fuzzy neural network (FNN) using gene expression profile data can select combinations of genes from thousands of genes, and is applicable to predict outcome for cancer patients after chemotherapy. However, wide clinical heterogeneity reduces the accuracy of prediction. To overcome this problem, we have proposed an FNN system based on majoritarian decision using multiple noninferior models. We used transcriptional profiling data, which were obtained from "Lymphochip" DNA microarrays (http://llmpp.nih.gov/DLBCL), reported by Rosenwald (N Engl J Med 2002; 346: 1937-47). When the data were analyzed by our FNN system, accuracy (73.4%) of outcome prediction using only 1 FNN model with 4 genes was higher than that (68.5%) of the Cox model using 17 genes. Higher accuracy (91%) was obtained when an FNN system with 9 noninferior models, consisting of 35 independent genes, was used. The genes selected by the system included genes that are informative in the prognosis of Diffuse large B-cell lymphoma (DLBCL), such as genes showing an expression pattern similar to that of CD10 and BCL-6 or similar to that of IRF-4 and BCL-4. We classified 220 DLBCL patients into 5 groups using the prediction results of 9 FNN models. These groups may correspond to DLBCL subtypes. In group A containing half of the 220 patients, patients with poor outcome were found to satisfy 2 rules, i.e., high expression of MAX dimerization with high expression of unknown A (LC_26146), or high expression of MAX dimerization with low expression of unknown B (LC_33144). The present paper is the first to describe the multiple noninferior FNN modeling system. This system is a powerful tool for predicting outcome and classifying patients, and is applicable to other heterogeneous diseases.

Diagnosing Parkinson's Diseases Using Fuzzy Neural System

PubMed Central

Abiyev, Rahib H.; Abizade, Sanan

2016-01-01

This study presents the design of the recognition system that will discriminate between healthy people and people with Parkinson's disease. A diagnosing of Parkinson's diseases is performed using fusion of the fuzzy system and neural networks. The structure and learning algorithms of the proposed fuzzy neural system (FNS) are presented. The approach described in this paper allows enhancing the capability of the designed system and efficiently distinguishing healthy individuals. It was proved through simulation of the system that has been performed using data obtained from UCI machine learning repository. A comparative study was carried out and the simulation results demonstrated that the proposed fuzzy neural system improves the recognition rate of the designed system. PMID:26881009

Family of new operations equivalency of neuro-fuzzy logic: optoelectronic realization and applications

NASA Astrophysics Data System (ADS)

Krasilenko, Vladimir G.; Nikolsky, Alexander I.; Yatskovsky, Victor I.; Ogorodnik, K. V.; Lischenko, Sergey

2002-07-01

The perspective of neural networks equivalental models (EM) base on vector-matrix procedure with basic operations of continuous and neuro-fuzzy logic (equivalence, absolute difference) are shown. Capacity on base EMs exceeded the amount of neurons in 2.5 times. This is larger than others neural networks paradigms. Amount neurons of this neural networks on base EMs may be 10 - 20 thousands. The base operations in EMs are normalized equivalency operations. The family of new operations equivalency and non-equivalency of neuro-fuzzy logic's, which we have elaborated on the based of such generalized operations of fuzzy-logic's as fuzzy negation, t-norm and s-norm are shown. Generalized rules of construction of new functions (operations) equivalency which uses relations of t-norm and s-norm to fuzzy negation are proposed. Among these elements the following should be underlined: (1) the element which fulfills the operation of limited difference; (2) the element which algebraic product (intensifier with controlled coefficient of transmission or multiplier of analog signals); (3) the element which fulfills a sample summarizing (uniting) of signals (including the one during normalizing). Synthesized structures which realize on the basic of these elements the whole spectrum of required operations: t-norm, s-norm and new operations equivalency are shown. These realization on the basic of new multifunctional optoelectronical BISPIN- devices (MOEBD) represent the circuit with constant and pulse optical input signals. They are modeling the operation of limited difference. These circuits realize frequency- dynamic neuron models and neural networks. Experimental results of these MOEBD and equivalency circuits, which fulfill the limited difference operation are discussed. For effective realization of neural networks on the basic of EMs as it is shown in report, picture elements are required as main nodes to implement element operations equivalence ('non-equivalence') of neuro-fuzzy

Comparison of Fuzzy-Based Models in Landslide Hazard Mapping

NASA Astrophysics Data System (ADS)

Mijani, N.; Neysani Samani, N.

2017-09-01

Landslide is one of the main geomorphic processes which effects on the development of prospect in mountainous areas and causes disastrous accidents. Landslide is an event which has different uncertain criteria such as altitude, slope, aspect, land use, vegetation density, precipitation, distance from the river and distance from the road network. This research aims to compare and evaluate different fuzzy-based models including Fuzzy Analytic Hierarchy Process (Fuzzy-AHP), Fuzzy Gamma and Fuzzy-OR. The main contribution of this paper reveals to the comprehensive criteria causing landslide hazard considering their uncertainties and comparison of different fuzzy-based models. The quantify of evaluation process are calculated by Density Ratio (DR) and Quality Sum (QS). The proposed methodology implemented in Sari, one of the city of Iran which has faced multiple landslide accidents in recent years due to the particular environmental conditions. The achieved results of accuracy assessment based on the quantifier strated that Fuzzy-AHP model has higher accuracy compared to other two models in landslide hazard zonation. Accuracy of zoning obtained from Fuzzy-AHP model is respectively 0.92 and 0.45 based on method Precision (P) and QS indicators. Based on obtained landslide hazard maps, Fuzzy-AHP, Fuzzy Gamma and Fuzzy-OR respectively cover 13, 26 and 35 percent of the study area with a very high risk level. Based on these findings, fuzzy-AHP model has been selected as the most appropriate method of zoning landslide in the city of Sari and the Fuzzy-gamma method with a minor difference is in the second order.

Risk Mapping of Cutaneous Leishmaniasis via a Fuzzy C Means-based Neuro-Fuzzy Inference System

NASA Astrophysics Data System (ADS)

Akhavan, P.; Karimi, M.; Pahlavani, P.

2014-10-01

Finding pathogenic factors and how they are spread in the environment has become a global demand, recently. Cutaneous Leishmaniasis (CL) created by Leishmania is a special parasitic disease which can be passed on to human through phlebotomus of vector-born. Studies show that economic situation, cultural issues, as well as environmental and ecological conditions can affect the prevalence of this disease. In this study, Data Mining is utilized in order to predict CL prevalence rate and obtain a risk map. This case is based on effective environmental parameters on CL and a Neuro-Fuzzy system was also used. Learning capacity of Neuro-Fuzzy systems in neural network on one hand and reasoning power of fuzzy systems on the other, make it very efficient to use. In this research, in order to predict CL prevalence rate, an adaptive Neuro-fuzzy inference system with fuzzy inference structure of fuzzy C Means clustering was applied to determine the initial membership functions. Regarding to high incidence of CL in Ilam province, counties of Ilam, Mehran, and Dehloran have been examined and evaluated. The CL prevalence rate was predicted in 2012 by providing effective environmental map and topography properties including temperature, moisture, annual, rainfall, vegetation and elevation. Results indicate that the model precision with fuzzy C Means clustering structure rises acceptable RMSE values of both training and checking data and support our analyses. Using the proposed data mining technology, the pattern of disease spatial distribution and vulnerable areas become identifiable and the map can be used by experts and decision makers of public health as a useful tool in management and optimal decision-making.

A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain.

PubMed

Hall, L O; Bensaid, A M; Clarke, L P; Velthuizen, R P; Silbiger, M S; Bezdek, J C

1992-01-01

Magnetic resonance (MR) brain section images are segmented and then synthetically colored to give visual representations of the original data with three approaches: the literal and approximate fuzzy c-means unsupervised clustering algorithms, and a supervised computational neural network. Initial clinical results are presented on normal volunteers and selected patients with brain tumors surrounded by edema. Supervised and unsupervised segmentation techniques provide broadly similar results. Unsupervised fuzzy algorithms were visually observed to show better segmentation when compared with raw image data for volunteer studies. For a more complex segmentation problem with tumor/edema or cerebrospinal fluid boundary, where the tissues have similar MR relaxation behavior, inconsistency in rating among experts was observed, with fuzz-c-means approaches being slightly preferred over feedforward cascade correlation results. Various facets of both approaches, such as supervised versus unsupervised learning, time complexity, and utility for the diagnostic process, are compared.

Fuzzy-logic based Q-Learning interference management algorithms in two-tier networks

NASA Astrophysics Data System (ADS)

Xu, Qiang; Xu, Zezhong; Li, Li; Zheng, Yan

2017-10-01

Unloading from macrocell network and enhancing coverage can be realized by deploying femtocells in the indoor scenario. However, the system performance of the two-tier network could be impaired by the co-tier and cross-tier interference. In this paper, a distributed resource allocation scheme is studied when each femtocell base station is self-governed and the resource cannot be assigned centrally through the gateway. A novel Q-Learning interference management scheme is proposed, that is divided into cooperative and independent part. In the cooperative algorithm, the interference information is exchanged between the cell-edge users which are classified by the fuzzy logic in the same cell. Meanwhile, we allocate the orthogonal subchannels to the high-rate cell-edge users to disperse the interference power when the data rate requirement is satisfied. The resource is assigned directly according to the minimum power principle in the independent algorithm. Simulation results are provided to demonstrate the significant performance improvements in terms of the average data rate, interference power and energy efficiency over the cutting-edge resource allocation algorithms.

Modeling and performance analysis using extended fuzzy-timing Petri nets for networked virtual environments.

PubMed

Zhou, Y; Murata, T; Defanti, T A

2000-01-01

Despite their attractive properties, networked virtual environments (net-VEs) are notoriously difficult to design, implement, and test due to the concurrency, real-time and networking features in these systems. Net-VEs demand high quality-of-service (QoS) requirements on the network to maintain natural and real-time interactions among users. The current practice for net-VE design is basically trial and error, empirical, and totally lacks formal methods. This paper proposes to apply a Petri net formal modeling technique to a net-VE-NICE (narrative immersive constructionist/collaborative environment), predict the net-VE performance based on simulation, and improve the net-VE performance. NICE is essentially a network of collaborative virtual reality systems called the CAVE-(CAVE automatic virtual environment). First, we introduce extended fuzzy-timing Petri net (EFTN) modeling and analysis techniques. Then, we present EFTN models of the CAVE, NICE, and transport layer protocol used in NICE: transmission control protocol (TCP). We show the possibility analysis based on the EFTN model for the CAVE. Then, by using these models and design/CPN as the simulation tool, we conducted various simulations to study real-time behavior, network effects and performance (latencies and jitters) of NICE. Our simulation results are consistent with experimental data.

Dynamics of Bacterial Gene Regulatory Networks.

PubMed

Shis, David L; Bennett, Matthew R; Igoshin, Oleg A

2018-05-20

The ability of bacterial cells to adjust their gene expression program in response to environmental perturbation is often critical for their survival. Recent experimental advances allowing us to quantitatively record gene expression dynamics in single cells and in populations coupled with mathematical modeling enable mechanistic understanding on how these responses are shaped by the underlying regulatory networks. Here, we review how the combination of local and global factors affect dynamical responses of gene regulatory networks. Our goal is to discuss the general principles that allow extrapolation from a few model bacteria to less understood microbes. We emphasize that, in addition to well-studied effects of network architecture, network dynamics are shaped by global pleiotropic effects and cell physiology.

Fuzzy logic applications to expert systems and control

NASA Technical Reports Server (NTRS)

Lea, Robert N.; Jani, Yashvant

1991-01-01

A considerable amount of work on the development of fuzzy logic algorithms and application to space related control problems has been done at the Johnson Space Center (JSC) over the past few years. Particularly, guidance control systems for space vehicles during proximity operations, learning systems utilizing neural networks, control of data processing during rendezvous navigation, collision avoidance algorithms, camera tracking controllers, and tether controllers have been developed utilizing fuzzy logic technology. Several other areas in which fuzzy sets and related concepts are being considered at JSC are diagnostic systems, control of robot arms, pattern recognition, and image processing. It has become evident, based on the commercial applications of fuzzy technology in Japan and China during the last few years, that this technology should be exploited by the government as well as private industry for energy savings.

Weighted gene co‑expression network analysis in identification of key genes and networks for ischemic‑reperfusion remodeling myocardium.

PubMed

Guo, Nan; Zhang, Nan; Yan, Liqiu; Lian, Zheng; Wang, Jiawang; Lv, Fengfeng; Wang, Yunfei; Cao, Xufen

2018-06-14

Acute myocardial infarction induces ventricular remodeling, which is implicated in dilated heart and heart failure. The pathogenical mechanism of myocardium remodeling remains to be elucidated. The aim of the present study was to identify key genes and networks for myocardium remodeling following ischemia‑reperfusion (IR). First, the mRNA expression data from the National Center for Biotechnology Information database were downloaded to identify differences in mRNA expression of the IR heart at days 2 and 7. Then, weighted gene co‑expression network analysis, hierarchical clustering, protein‑protein interaction (PPI) network, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway were used to identify key genes and networks for the heart remodeling process following IR. A total of 3,321 differentially expressed genes were identified during the heart remodeling process. A total of 6 modules were identified through gene co‑expression network analysis. GO and KEGG analysis results suggested that each module represented a different biological function and was associated with different pathways. Finally, hub genes of each module were identified by PPI network construction. The present study revealed that heart remodeling following IR is a complicated process, involving extracellular matrix organization, neural development, apoptosis and energy metabolism. The dysregulated genes, including SRC proto‑oncogene, non‑receptor tyrosine kinase, discs large MAGUK scaffold protein 1, ATP citrate lyase, RAN, member RAS oncogene family, tumor protein p53, and polo like kinase 2, may be essential for heart remodeling following IR and may be used as potential targets for the inhibition of heart remodeling following acute myocardial infarction.

Finding gene regulatory network candidates using the gene expression knowledge base.

PubMed

Venkatesan, Aravind; Tripathi, Sushil; Sanz de Galdeano, Alejandro; Blondé, Ward; Lægreid, Astrid; Mironov, Vladimir; Kuiper, Martin

2014-12-10

Network-based approaches for the analysis of large-scale genomics data have become well established. Biological networks provide a knowledge scaffold against which the patterns and dynamics of 'omics' data can be interpreted. The background information required for the construction of such networks is often dispersed across a multitude of knowledge bases in a variety of formats. The seamless integration of this information is one of the main challenges in bioinformatics. The Semantic Web offers powerful technologies for the assembly of integrated knowledge bases that are computationally comprehensible, thereby providing a potentially powerful resource for constructing biological networks and network-based analysis. We have developed the Gene eXpression Knowledge Base (GeXKB), a semantic web technology based resource that contains integrated knowledge about gene expression regulation. To affirm the utility of GeXKB we demonstrate how this resource can be exploited for the identification of candidate regulatory network proteins. We present four use cases that were designed from a biological perspective in order to find candidate members relevant for the gastrin hormone signaling network model. We show how a combination of specific query definitions and additional selection criteria derived from gene expression data and prior knowledge concerning candidate proteins can be used to retrieve a set of proteins that constitute valid candidates for regulatory network extensions. Semantic web technologies provide the means for processing and integrating various heterogeneous information sources. The GeXKB offers biologists such an integrated knowledge resource, allowing them to address complex biological questions pertaining to gene expression. This work illustrates how GeXKB can be used in combination with gene expression results and literature information to identify new potential candidates that may be considered for extending a gene regulatory network.

Genes2Networks: connecting lists of gene symbols using mammalian protein interactions databases.

PubMed

Berger, Seth I; Posner, Jeremy M; Ma'ayan, Avi

2007-10-04

In recent years, mammalian protein-protein interaction network databases have been developed. The interactions in these databases are either extracted manually from low-throughput experimental biomedical research literature, extracted automatically from literature using techniques such as natural language processing (NLP), generated experimentally using high-throughput methods such as yeast-2-hybrid screens, or interactions are predicted using an assortment of computational approaches. Genes or proteins identified as significantly changing in proteomic experiments, or identified as susceptibility disease genes in genomic studies, can be placed in the context of protein interaction networks in order to assign these genes and proteins to pathways and protein complexes. Genes2Networks is a software system that integrates the content of ten mammalian interaction network datasets. Filtering techniques to prune low-confidence interactions were implemented. Genes2Networks is delivered as a web-based service using AJAX. The system can be used to extract relevant subnetworks created from "seed" lists of human Entrez gene symbols. The output includes a dynamic linkable three color web-based network map, with a statistical analysis report that identifies significant intermediate nodes used to connect the seed list. Genes2Networks is powerful web-based software that can help experimental biologists to interpret lists of genes and proteins such as those commonly produced through genomic and proteomic experiments, as well as lists of genes and proteins associated with disease processes. This system can be used to find relationships between genes and proteins from seed lists, and predict additional genes or proteins that may play key roles in common pathways or protein complexes.

Modeling of a 5-cell direct methanol fuel cell using adaptive-network-based fuzzy inference systems

NASA Astrophysics Data System (ADS)

Wang, Rongrong; Qi, Liang; Xie, Xiaofeng; Ding, Qingqing; Li, Chunwen; Ma, ChenChi M.

The methanol concentrations, temperature and current were considered as inputs, the cell voltage was taken as output, and the performance of a direct methanol fuel cell (DMFC) was modeled by adaptive-network-based fuzzy inference systems (ANFIS). The artificial neural network (ANN) and polynomial-based models were selected to be compared with the ANFIS in respect of quality and accuracy. Based on the ANFIS model obtained, the characteristics of the DMFC were studied. The results show that temperature and methanol concentration greatly affect the performance of the DMFC. Within a restricted current range, the methanol concentration does not greatly affect the stack voltage. In order to obtain higher fuel utilization efficiency, the methanol concentrations and temperatures should be adjusted according to the load on the system.

Naturally-Emerging Technology-Based Leadership Roles in Three Independent Schools: A Social Network-Based Case Study Using Fuzzy Set Qualitative Comparative Analysis

ERIC Educational Resources Information Center

Velastegui, Pamela J.

2013-01-01

This hypothesis-generating case study investigates the naturally emerging roles of technology brokers and technology leaders in three independent schools in New York involving 92 school educators. A multiple and mixed method design utilizing Social Network Analysis (SNA) and fuzzy set Qualitative Comparative Analysis (FSQCA) involved gathering…

Coastal vulnerability assessment using Fuzzy Logic and Bayesian Belief Network approaches

NASA Astrophysics Data System (ADS)

Valentini, Emiliana; Nguyen Xuan, Alessandra; Filipponi, Federico; Taramelli, Andrea

2017-04-01

Natural hazards such as sea surge are threatening low-lying coastal plains. In order to deal with disturbances a deeper understanding of benefits deriving from ecosystem services assessment, management and planning can contribute to enhance the resilience of coastal systems. In this frame assessing current and future vulnerability is a key concern of many Systems Of Systems SOS (social, ecological, institutional) that deals with several challenges like the definition of Essential Variables (EVs) able to synthesize the required information, the assignment of different weight to be attributed to each considered variable, the selection of method for combining the relevant variables. It is widely recognized that ecosystems contribute to human wellbeing and then their conservation increases the resilience capacities and could play a key role in reducing climate related risk and thus physical and economic losses. A way to fully exploit ecosystems potential, i.e. their so called ecopotential (see H2020 EU funded project "ECOPOTENTIAL"), is the Ecosystem based Adaptation (EbA): the use of ecosystem services as part of an adaptation strategy. In order to provide insight in understanding regulating ecosystem services to surge and which variables influence them and to make the best use of available data and information (EO products, in situ data and modelling), we propose a multi-component surge vulnerability assessment, focusing on coastal sandy dunes as natural barriers. The aim is to combine together eco-geomorphological and socio-economic variables with the hazard component on the base of different approaches: 1) Fuzzy Logic; 2) Bayesian Belief Networks (BBN). The Fuzzy Logic approach is very useful to get a spatialized information and it can easily combine variables coming from different sources. It provides information on vulnerability moving along-shore and across-shore (beach-dune transect), highlighting the variability of vulnerability conditions in the spatial

Modeling gene regulatory network motifs using statecharts

PubMed Central

2012-01-01

Background Gene regulatory networks are widely used by biologists to describe the interactions among genes, proteins and other components at the intra-cellular level. Recently, a great effort has been devoted to give gene regulatory networks a formal semantics based on existing computational frameworks. For this purpose, we consider Statecharts, which are a modular, hierarchical and executable formal model widely used to represent software systems. We use Statecharts for modeling small and recurring patterns of interactions in gene regulatory networks, called motifs. Results We present an improved method for modeling gene regulatory network motifs using Statecharts and we describe the successful modeling of several motifs, including those which could not be modeled or whose models could not be distinguished using the method of a previous proposal. We model motifs in an easy and intuitive way by taking advantage of the visual features of Statecharts. Our modeling approach is able to simulate some interesting temporal properties of gene regulatory network motifs: the delay in the activation and the deactivation of the "output" gene in the coherent type-1 feedforward loop, the pulse in the incoherent type-1 feedforward loop, the bistability nature of double positive and double negative feedback loops, the oscillatory behavior of the negative feedback loop, and the "lock-in" effect of positive autoregulation. Conclusions We present a Statecharts-based approach for the modeling of gene regulatory network motifs in biological systems. The basic motifs used to build more complex networks (that is, simple regulation, reciprocal regulation, feedback loop, feedforward loop, and autoregulation) can be faithfully described and their temporal dynamics can be analyzed. PMID:22536967

Hybrid supervisory control using recurrent fuzzy neural network for tracking periodic inputs.

PubMed

Lin, F J; Wai, R J; Hong, C M

2001-01-01

A hybrid supervisory control system using a recurrent fuzzy neural network (RFNN) is proposed to control the mover of a permanent magnet linear synchronous motor (PMLSM) servo drive for the tracking of periodic reference inputs. First, the field-oriented mechanism is applied to formulate the dynamic equation of the PMLSM. Then, a hybrid supervisory control system, which combines a supervisory control system and an intelligent control system, is proposed to control the mover of the PMLSM for periodic motion. The supervisory control law is designed based on the uncertainty bounds of the controlled system to stabilize the system states around a predefined bound region. Since the supervisory control law will induce excessive and chattering control effort, the intelligent control system is introduced to smooth and reduce the control effort when the system states are inside the predefined bound region. In the intelligent control system, the RFNN control is the main tracking controller which is used to mimic a idea control law and a compensated control is proposed to compensate the difference between the idea control law and the RFNN control. The RFNN has the merits of fuzzy inference, dynamic mapping and fast convergence speed, In addition, an online parameter training methodology, which is derived using the Lyapunov stability theorem and the gradient descent method, is proposed to increase the learning capability of the RFNN. The proposed hybrid supervisory control system using RFNN can track various periodic reference inputs effectively with robust control performance.

Coexpression network based on natural variation in human gene expression reveals gene interactions and functions

PubMed Central

Nayak, Renuka R.; Kearns, Michael; Spielman, Richard S.; Cheung, Vivian G.

2009-01-01

Genes interact in networks to orchestrate cellular processes. Analysis of these networks provides insights into gene interactions and functions. Here, we took advantage of normal variation in human gene expression to infer gene networks, which we constructed using correlations in expression levels of more than 8.5 million gene pairs in immortalized B cells from three independent samples. The resulting networks allowed us to identify biological processes and gene functions. Among the biological pathways, we found processes such as translation and glycolysis that co-occur in the same subnetworks. We predicted the functions of poorly characterized genes, including CHCHD2 and TMEM111, and provided experimental evidence that TMEM111 is part of the endoplasmic reticulum-associated secretory pathway. We also found that IFIH1, a susceptibility gene of type 1 diabetes, interacts with YES1, which plays a role in glucose transport. Furthermore, genes that predispose to the same diseases are clustered nonrandomly in the coexpression network, suggesting that networks can provide candidate genes that influence disease susceptibility. Therefore, our analysis of gene coexpression networks offers information on the role of human genes in normal and disease processes. PMID:19797678

Convergent evolution of gene networks by single-gene duplications in higher eukaryotes.

PubMed

Amoutzias, Gregory D; Robertson, David L; Oliver, Stephen G; Bornberg-Bauer, Erich

2004-03-01

By combining phylogenetic, proteomic and structural information, we have elucidated the evolutionary driving forces for the gene-regulatory interaction networks of basic helix-loop-helix transcription factors. We infer that recurrent events of single-gene duplication and domain rearrangement repeatedly gave rise to distinct networks with almost identical hub-based topologies, and multiple activators and repressors. We thus provide the first empirical evidence for scale-free protein networks emerging through single-gene duplications, the dominant importance of molecular modularity in the bottom-up construction of complex biological entities, and the convergent evolution of networks.

A Hybrid Neuro-Fuzzy Model For Integrating Large Earth-Science Datasets

NASA Astrophysics Data System (ADS)

Porwal, A.; Carranza, J.; Hale, M.

2004-12-01

A GIS-based hybrid neuro-fuzzy approach to integration of large earth-science datasets for mineral prospectivity mapping is described. It implements a Takagi-Sugeno type fuzzy inference system in the framework of a four-layered feed-forward adaptive neural network. Each unique combination of the datasets is considered a feature vector whose components are derived by knowledge-based ordinal encoding of the constituent datasets. A subset of feature vectors with a known output target vector (i.e., unique conditions known to be associated with either a mineralized or a barren location) is used for the training of an adaptive neuro-fuzzy inference system. Training involves iterative adjustment of parameters of the adaptive neuro-fuzzy inference system using a hybrid learning procedure for mapping each training vector to its output target vector with minimum sum of squared error. The trained adaptive neuro-fuzzy inference system is used to process all feature vectors. The output for each feature vector is a value that indicates the extent to which a feature vector belongs to the mineralized class or the barren class. These values are used to generate a prospectivity map. The procedure is demonstrated by an application to regional-scale base metal prospectivity mapping in a study area located in the Aravalli metallogenic province (western India). A comparison of the hybrid neuro-fuzzy approach with pure knowledge-driven fuzzy and pure data-driven neural network approaches indicates that the former offers a superior method for integrating large earth-science datasets for predictive spatial mathematical modelling.

Gene networks and the evolution of plant morphology.

PubMed

Das Gupta, Mainak; Tsiantis, Miltos

2018-06-06

Elaboration of morphology depends on the precise orchestration of gene expression by key regulatory genes. The hierarchy and relationship among the participating genes is commonly known as gene regulatory network (GRN). Therefore, the evolution of morphology ultimately occurs by the rewiring of gene network structures or by the co-option of gene networks to novel domains. The availability of high-resolution expression data combined with powerful statistical tools have opened up new avenues to formulate and test hypotheses on how diverse gene networks influence trait development and diversity. Here we summarize recent studies based on both big-data and genetics approaches to understand the evolution of plant form and physiology. We also discuss recent genome-wide investigations on how studying open-chromatin regions may help study the evolution of gene expression patterns. Copyright © 2018. Published by Elsevier Ltd.

Solving fully fuzzy transportation problem using pentagonal fuzzy numbers

NASA Astrophysics Data System (ADS)

Maheswari, P. Uma; Ganesan, K.

2018-04-01

In this paper, we propose a simple approach for the solution of fuzzy transportation problem under fuzzy environment in which the transportation costs, supplies at sources and demands at destinations are represented by pentagonal fuzzy numbers. The fuzzy transportation problem is solved without converting to its equivalent crisp form using a robust ranking technique and a new fuzzy arithmetic on pentagonal fuzzy numbers. To illustrate the proposed approach a numerical example is provided.

Fuzzy logic of Aristotelian forms

DOE Office of Scientific and Technical Information (OSTI.GOV)

Perlovsky, L.I.

1996-12-31

Model-based approaches to pattern recognition and machine vision have been proposed to overcome the exorbitant training requirements of earlier computational paradigms. However, uncertainties in data were found to lead to a combinatorial explosion of the computational complexity. This issue is related here to the roles of a priori knowledge vs. adaptive learning. What is the a-priori knowledge representation that supports learning? I introduce Modeling Field Theory (MFT), a model-based neural network whose adaptive learning is based on a priori models. These models combine deterministic, fuzzy, and statistical aspects to account for a priori knowledge, its fuzzy nature, and data uncertainties.more » In the process of learning, a priori fuzzy concepts converge to crisp or probabilistic concepts. The MFT is a convergent dynamical system of only linear computational complexity. Fuzzy logic turns out to be essential for reducing the combinatorial complexity to linear one. I will discuss the relationship of the new computational paradigm to two theories due to Aristotle: theory of Forms and logic. While theory of Forms argued that the mind cannot be based on ready-made a priori concepts, Aristotelian logic operated with just such concepts. I discuss an interpretation of MFT suggesting that its fuzzy logic, combining a-priority and adaptivity, implements Aristotelian theory of Forms (theory of mind). Thus, 2300 years after Aristotle, a logic is developed suitable for his theory of mind.« less

Finding pathway-modulating genes from a novel Ontology Fingerprint-derived gene network

PubMed Central

Qin, Tingting; Matmati, Nabil; Tsoi, Lam C.; Mohanty, Bidyut K.; Gao, Nan; Tang, Jijun; Lawson, Andrew B.; Hannun, Yusuf A.; Zheng, W. Jim

2014-01-01

To enhance our knowledge regarding biological pathway regulation, we took an integrated approach, using the biomedical literature, ontologies, network analyses and experimental investigation to infer novel genes that could modulate biological pathways. We first constructed a novel gene network via a pairwise comparison of all yeast genes’ Ontology Fingerprints—a set of Gene Ontology terms overrepresented in the PubMed abstracts linked to a gene along with those terms’ corresponding enrichment P-values. The network was further refined using a Bayesian hierarchical model to identify novel genes that could potentially influence the pathway activities. We applied this method to the sphingolipid pathway in yeast and found that many top-ranked genes indeed displayed altered sphingolipid pathway functions, initially measured by their sensitivity to myriocin, an inhibitor of de novo sphingolipid biosynthesis. Further experiments confirmed the modulation of the sphingolipid pathway by one of these genes, PFA4, encoding a palmitoyl transferase. Comparative analysis showed that few of these novel genes could be discovered by other existing methods. Our novel gene network provides a unique and comprehensive resource to study pathway modulations and systems biology in general. PMID:25063300

Combinatorial explosion in model gene networks

NASA Astrophysics Data System (ADS)

Edwards, R.; Glass, L.

2000-09-01

The explosive growth in knowledge of the genome of humans and other organisms leaves open the question of how the functioning of genes in interacting networks is coordinated for orderly activity. One approach to this problem is to study mathematical properties of abstract network models that capture the logical structures of gene networks. The principal issue is to understand how particular patterns of activity can result from particular network structures, and what types of behavior are possible. We study idealized models in which the logical structure of the network is explicitly represented by Boolean functions that can be represented by directed graphs on n-cubes, but which are continuous in time and described by differential equations, rather than being updated synchronously via a discrete clock. The equations are piecewise linear, which allows significant analysis and facilitates rapid integration along trajectories. We first give a combinatorial solution to the question of how many distinct logical structures exist for n-dimensional networks, showing that the number increases very rapidly with n. We then outline analytic methods that can be used to establish the existence, stability and periods of periodic orbits corresponding to particular cycles on the n-cube. We use these methods to confirm the existence of limit cycles discovered in a sample of a million randomly generated structures of networks of 4 genes. Even with only 4 genes, at least several hundred different patterns of stable periodic behavior are possible, many of them surprisingly complex. We discuss ways of further classifying these periodic behaviors, showing that small mutations (reversal of one or a few edges on the n-cube) need not destroy the stability of a limit cycle. Although these networks are very simple as models of gene networks, their mathematical transparency reveals relationships between structure and behavior, they suggest that the possibilities for orderly dynamics in such

Combinatorial explosion in model gene networks.

PubMed

Edwards, R.; Glass, L.

2000-09-01

The explosive growth in knowledge of the genome of humans and other organisms leaves open the question of how the functioning of genes in interacting networks is coordinated for orderly activity. One approach to this problem is to study mathematical properties of abstract network models that capture the logical structures of gene networks. The principal issue is to understand how particular patterns of activity can result from particular network structures, and what types of behavior are possible. We study idealized models in which the logical structure of the network is explicitly represented by Boolean functions that can be represented by directed graphs on n-cubes, but which are continuous in time and described by differential equations, rather than being updated synchronously via a discrete clock. The equations are piecewise linear, which allows significant analysis and facilitates rapid integration along trajectories. We first give a combinatorial solution to the question of how many distinct logical structures exist for n-dimensional networks, showing that the number increases very rapidly with n. We then outline analytic methods that can be used to establish the existence, stability and periods of periodic orbits corresponding to particular cycles on the n-cube. We use these methods to confirm the existence of limit cycles discovered in a sample of a million randomly generated structures of networks of 4 genes. Even with only 4 genes, at least several hundred different patterns of stable periodic behavior are possible, many of them surprisingly complex. We discuss ways of further classifying these periodic behaviors, showing that small mutations (reversal of one or a few edges on the n-cube) need not destroy the stability of a limit cycle. Although these networks are very simple as models of gene networks, their mathematical transparency reveals relationships between structure and behavior, they suggest that the possibilities for orderly dynamics in such

Gene expression complex networks: synthesis, identification, and analysis.

PubMed

Lopes, Fabrício M; Cesar, Roberto M; Costa, Luciano Da F

2011-10-01

Thanks to recent advances in molecular biology, allied to an ever increasing amount of experimental data, the functional state of thousands of genes can now be extracted simultaneously by using methods such as cDNA microarrays and RNA-Seq. Particularly important related investigations are the modeling and identification of gene regulatory networks from expression data sets. Such a knowledge is fundamental for many applications, such as disease treatment, therapeutic intervention strategies and drugs design, as well as for planning high-throughput new experiments. Methods have been developed for gene networks modeling and identification from expression profiles. However, an important open problem regards how to validate such approaches and its results. This work presents an objective approach for validation of gene network modeling and identification which comprises the following three main aspects: (1) Artificial Gene Networks (AGNs) model generation through theoretical models of complex networks, which is used to simulate temporal expression data; (2) a computational method for gene network identification from the simulated data, which is founded on a feature selection approach where a target gene is fixed and the expression profile is observed for all other genes in order to identify a relevant subset of predictors; and (3) validation of the identified AGN-based network through comparison with the original network. The proposed framework allows several types of AGNs to be generated and used in order to simulate temporal expression data. The results of the network identification method can then be compared to the original network in order to estimate its properties and accuracy. Some of the most important theoretical models of complex networks have been assessed: the uniformly-random Erdös-Rényi (ER), the small-world Watts-Strogatz (WS), the scale-free Barabási-Albert (BA), and geographical networks (GG). The experimental results indicate that the inference

Design of fuzzy cognitive maps using neural networks for predicting chaotic time series.

PubMed

Song, H J; Miao, C Y; Shen, Z Q; Roel, W; Maja, D H; Francky, C

2010-12-01

As a powerful paradigm for knowledge representation and a simulation mechanism applicable to numerous research and application fields, Fuzzy Cognitive Maps (FCMs) have attracted a great deal of attention from various research communities. However, the traditional FCMs do not provide efficient methods to determine the states of the investigated system and to quantify causalities which are the very foundation of the FCM theory. Therefore in many cases, constructing FCMs for complex causal systems greatly depends on expert knowledge. The manually developed models have a substantial shortcoming due to model subjectivity and difficulties with accessing its reliability. In this paper, we propose a fuzzy neural network to enhance the learning ability of FCMs so that the automatic determination of membership functions and quantification of causalities can be incorporated with the inference mechanism of conventional FCMs. In this manner, FCM models of the investigated systems can be automatically constructed from data, and therefore are independent of the experts. Furthermore, we employ mutual subsethood to define and describe the causalities in FCMs. It provides more explicit interpretation for causalities in FCMs and makes the inference process easier to understand. To validate the performance, the proposed approach is tested in predicting chaotic time series. The simulation studies show the effectiveness of the proposed approach. Copyright © 2010 Elsevier Ltd. All rights reserved.

QoE collaborative evaluation method based on fuzzy clustering heuristic algorithm.

PubMed

Bao, Ying; Lei, Weimin; Zhang, Wei; Zhan, Yuzhuo

2016-01-01

At present, to realize or improve the quality of experience (QoE) is a major goal for network media transmission service, and QoE evaluation is the basis for adjusting the transmission control mechanism. Therefore, a kind of QoE collaborative evaluation method based on fuzzy clustering heuristic algorithm is proposed in this paper, which is concentrated on service score calculation at the server side. The server side collects network transmission quality of service (QoS) parameter, node location data, and user expectation value from client feedback information. Then it manages the historical data in database through the "big data" process mode, and predicts user score according to heuristic rules. On this basis, it completes fuzzy clustering analysis, and generates service QoE score and management message, which will be finally fed back to clients. Besides, this paper mainly discussed service evaluation generative rules, heuristic evaluation rules and fuzzy clustering analysis methods, and presents service-based QoE evaluation processes. The simulation experiments have verified the effectiveness of QoE collaborative evaluation method based on fuzzy clustering heuristic rules.

Finding pathway-modulating genes from a novel Ontology Fingerprint-derived gene network.

PubMed

Qin, Tingting; Matmati, Nabil; Tsoi, Lam C; Mohanty, Bidyut K; Gao, Nan; Tang, Jijun; Lawson, Andrew B; Hannun, Yusuf A; Zheng, W Jim

2014-10-01

To enhance our knowledge regarding biological pathway regulation, we took an integrated approach, using the biomedical literature, ontologies, network analyses and experimental investigation to infer novel genes that could modulate biological pathways. We first constructed a novel gene network via a pairwise comparison of all yeast genes' Ontology Fingerprints--a set of Gene Ontology terms overrepresented in the PubMed abstracts linked to a gene along with those terms' corresponding enrichment P-values. The network was further refined using a Bayesian hierarchical model to identify novel genes that could potentially influence the pathway activities. We applied this method to the sphingolipid pathway in yeast and found that many top-ranked genes indeed displayed altered sphingolipid pathway functions, initially measured by their sensitivity to myriocin, an inhibitor of de novo sphingolipid biosynthesis. Further experiments confirmed the modulation of the sphingolipid pathway by one of these genes, PFA4, encoding a palmitoyl transferase. Comparative analysis showed that few of these novel genes could be discovered by other existing methods. Our novel gene network provides a unique and comprehensive resource to study pathway modulations and systems biology in general. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

Computational gene network study on antibiotic resistance genes of Acinetobacter baumannii.

PubMed

Anitha, P; Anbarasu, Anand; Ramaiah, Sudha

2014-05-01

Multi Drug Resistance (MDR) in Acinetobacter baumannii is one of the major threats for emerging nosocomial infections in hospital environment. Multidrug-resistance in A. baumannii may be due to the implementation of multi-combination resistance mechanisms such as β-lactamase synthesis, Penicillin-Binding Proteins (PBPs) changes, alteration in porin proteins and in efflux pumps against various existing classes of antibiotics. Multiple antibiotic resistance genes are involved in MDR. These resistance genes are transferred through plasmids, which are responsible for the dissemination of antibiotic resistance among Acinetobacter spp. In addition, these resistance genes may also have a tendency to interact with each other or with their gene products. Therefore, it becomes necessary to understand the impact of these interactions in antibiotic resistance mechanism. Hence, our study focuses on protein and gene network analysis on various resistance genes, to elucidate the role of the interacting proteins and to study their functional contribution towards antibiotic resistance. From the search tool for the retrieval of interacting gene/protein (STRING), a total of 168 functional partners for 15 resistance genes were extracted based on the confidence scoring system. The network study was then followed up with functional clustering of associated partners using molecular complex detection (MCODE). Later, we selected eight efficient clusters based on score. Interestingly, the associated protein we identified from the network possessed greater functional similarity with known resistance genes. This network-based approach on resistance genes of A. baumannii could help in identifying new genes/proteins and provide clues on their association in antibiotic resistance. Copyright © 2014 Elsevier Ltd. All rights reserved.

Fuzzy logic in autonomous orbital operations

NASA Technical Reports Server (NTRS)

Lea, Robert N.; Jani, Yashvant

1991-01-01

Fuzzy logic can be used advantageously in autonomous orbital operations that require the capability of handling imprecise measurements from sensors. Several applications are underway to investigate fuzzy logic approaches and develop guidance and control algorithms for autonomous orbital operations. Translational as well as rotational control of a spacecraft have been demonstrated using space shuttle simulations. An approach to a camera tracking system has been developed to support proximity operations and traffic management around the Space Station Freedom. Pattern recognition and object identification algorithms currently under development will become part of this camera system at an appropriate level in the future. A concept to control environment and life support systems for large Lunar based crew quarters is also under development. Investigations in the area of reinforcement learning, utilizing neural networks, combined with a fuzzy logic controller, are planned as a joint project with the Ames Research Center.

Learning a Markov Logic network for supervised gene regulatory network inference

PubMed Central

2013-01-01

Background Gene regulatory network inference remains a challenging problem in systems biology despite the numerous approaches that have been proposed. When substantial knowledge on a gene regulatory network is already available, supervised network inference is appropriate. Such a method builds a binary classifier able to assign a class (Regulation/No regulation) to an ordered pair of genes. Once learnt, the pairwise classifier can be used to predict new regulations. In this work, we explore the framework of Markov Logic Networks (MLN) that combine features of probabilistic graphical models with the expressivity of first-order logic rules. Results We propose to learn a Markov Logic network, e.g. a set of weighted rules that conclude on the predicate “regulates”, starting from a known gene regulatory network involved in the switch proliferation/differentiation of keratinocyte cells, a set of experimental transcriptomic data and various descriptions of genes all encoded into first-order logic. As training data are unbalanced, we use asymmetric bagging to learn a set of MLNs. The prediction of a new regulation can then be obtained by averaging predictions of individual MLNs. As a side contribution, we propose three in silico tests to assess the performance of any pairwise classifier in various network inference tasks on real datasets. A first test consists of measuring the average performance on balanced edge prediction problem; a second one deals with the ability of the classifier, once enhanced by asymmetric bagging, to update a given network. Finally our main result concerns a third test that measures the ability of the method to predict regulations with a new set of genes. As expected, MLN, when provided with only numerical discretized gene expression data, does not perform as well as a pairwise SVM in terms of AUPR. However, when a more complete description of gene properties is provided by heterogeneous sources, MLN achieves the same performance as a black

Learning a Markov Logic network for supervised gene regulatory network inference.

PubMed

Brouard, Céline; Vrain, Christel; Dubois, Julie; Castel, David; Debily, Marie-Anne; d'Alché-Buc, Florence

2013-09-12

Gene regulatory network inference remains a challenging problem in systems biology despite the numerous approaches that have been proposed. When substantial knowledge on a gene regulatory network is already available, supervised network inference is appropriate. Such a method builds a binary classifier able to assign a class (Regulation/No regulation) to an ordered pair of genes. Once learnt, the pairwise classifier can be used to predict new regulations. In this work, we explore the framework of Markov Logic Networks (MLN) that combine features of probabilistic graphical models with the expressivity of first-order logic rules. We propose to learn a Markov Logic network, e.g. a set of weighted rules that conclude on the predicate "regulates", starting from a known gene regulatory network involved in the switch proliferation/differentiation of keratinocyte cells, a set of experimental transcriptomic data and various descriptions of genes all encoded into first-order logic. As training data are unbalanced, we use asymmetric bagging to learn a set of MLNs. The prediction of a new regulation can then be obtained by averaging predictions of individual MLNs. As a side contribution, we propose three in silico tests to assess the performance of any pairwise classifier in various network inference tasks on real datasets. A first test consists of measuring the average performance on balanced edge prediction problem; a second one deals with the ability of the classifier, once enhanced by asymmetric bagging, to update a given network. Finally our main result concerns a third test that measures the ability of the method to predict regulations with a new set of genes. As expected, MLN, when provided with only numerical discretized gene expression data, does not perform as well as a pairwise SVM in terms of AUPR. However, when a more complete description of gene properties is provided by heterogeneous sources, MLN achieves the same performance as a black-box model such as a

Classification of air quality using fuzzy synthetic multiplication.

PubMed

Abdullah, Lazim; Khalid, Noor Dalina

2012-11-01

Proper identification of environment's air quality based on limited observations is an essential task to meet the goals of environmental management. Various classification methods have been used to estimate the change of air quality status and health. However, discrepancies frequently arise from the lack of clear distinction between each air quality, the uncertainty in the quality criteria employed and the vagueness or fuzziness embedded in the decision-making output values. Owing to inherent imprecision, difficulties always exist in some conventional methodologies when describing integrated air quality conditions with respect to various pollutants. Therefore, this paper presents two fuzzy multiplication synthetic techniques to establish classification of air quality. The fuzzy multiplication technique empowers the max-min operations in "or" and "and" in executing the fuzzy arithmetic operations. Based on a set of air pollutants data carbon monoxide, sulfur dioxide, nitrogen dioxide, ozone, and particulate matter (PM(10)) collected from a network of 51 stations in Klang Valley, East Malaysia, Sabah, and Sarawak were utilized in this evaluation. The two fuzzy multiplication techniques consistently classified Malaysia's air quality as "good." The findings indicated that the techniques may have successfully harmonized inherent discrepancies and interpret complex conditions. It was demonstrated that fuzzy synthetic multiplication techniques are quite appropriate techniques for air quality management.

Resistance Genes in Global Crop Breeding Networks.

PubMed

Garrett, K A; Andersen, K F; Asche, F; Bowden, R L; Forbes, G A; Kulakow, P A; Zhou, B

2017-10-01

Resistance genes are a major tool for managing crop diseases. The networks of crop breeders who exchange resistance genes and deploy them in varieties help to determine the global landscape of resistance and epidemics, an important system for maintaining food security. These networks function as a complex adaptive system, with associated strengths and vulnerabilities, and implications for policies to support resistance gene deployment strategies. Extensions of epidemic network analysis can be used to evaluate the multilayer agricultural networks that support and influence crop breeding networks. Here, we evaluate the general structure of crop breeding networks for cassava, potato, rice, and wheat. All four are clustered due to phytosanitary and intellectual property regulations, and linked through CGIAR hubs. Cassava networks primarily include public breeding groups, whereas others are more mixed. These systems must adapt to global change in climate and land use, the emergence of new diseases, and disruptive breeding technologies. Research priorities to support policy include how best to maintain both diversity and redundancy in the roles played by individual crop breeding groups (public versus private and global versus local), and how best to manage connectivity to optimize resistance gene deployment while avoiding risks to the useful life of resistance genes. [Formula: see text] Copyright © 2017 The Author(s). This is an open access article distributed under the CC BY 4.0 International license .

Markov State Models of gene regulatory networks.

PubMed

Chu, Brian K; Tse, Margaret J; Sato, Royce R; Read, Elizabeth L

2017-02-06

Gene regulatory networks with dynamics characterized by multiple stable states underlie cell fate-decisions. Quantitative models that can link molecular-level knowledge of gene regulation to a global understanding of network dynamics have the potential to guide cell-reprogramming strategies. Networks are often modeled by the stochastic Chemical Master Equation, but methods for systematic identification of key properties of the global dynamics are currently lacking. The method identifies the number, phenotypes, and lifetimes of long-lived states for a set of common gene regulatory network models. Application of transition path theory to the constructed Markov State Model decomposes global dynamics into a set of dominant transition paths and associated relative probabilities for stochastic state-switching. In this proof-of-concept study, we found that the Markov State Model provides a general framework for analyzing and visualizing stochastic multistability and state-transitions in gene networks. Our results suggest that this framework-adopted from the field of atomistic Molecular Dynamics-can be a useful tool for quantitative Systems Biology at the network scale.

Investigating the Effects of Imputation Methods for Modelling Gene Networks Using a Dynamic Bayesian Network from Gene Expression Data

PubMed Central

CHAI, Lian En; LAW, Chow Kuan; MOHAMAD, Mohd Saberi; CHONG, Chuii Khim; CHOON, Yee Wen; DERIS, Safaai; ILLIAS, Rosli Md

2014-01-01

Background: Gene expression data often contain missing expression values. Therefore, several imputation methods have been applied to solve the missing values, which include k-nearest neighbour (kNN), local least squares (LLS), and Bayesian principal component analysis (BPCA). However, the effects of these imputation methods on the modelling of gene regulatory networks from gene expression data have rarely been investigated and analysed using a dynamic Bayesian network (DBN). Methods: In the present study, we separately imputed datasets of the Escherichia coli S.O.S. DNA repair pathway and the Saccharomyces cerevisiae cell cycle pathway with kNN, LLS, and BPCA, and subsequently used these to generate gene regulatory networks (GRNs) using a discrete DBN. We made comparisons on the basis of previous studies in order to select the gene network with the least error. Results: We found that BPCA and LLS performed better on larger networks (based on the S. cerevisiae dataset), whereas kNN performed better on smaller networks (based on the E. coli dataset). Conclusion: The results suggest that the performance of each imputation method is dependent on the size of the dataset, and this subsequently affects the modelling of the resultant GRNs using a DBN. In addition, on the basis of these results, a DBN has the capacity to discover potential edges, as well as display interactions, between genes. PMID:24876803

Improving Classification of Cancer and Mining Biomarkers from Gene Expression Profiles Using Hybrid Optimization Algorithms and Fuzzy Support Vector Machine

PubMed Central

Moteghaed, Niloofar Yousefi; Maghooli, Keivan; Garshasbi, Masoud

2018-01-01

Background: Gene expression data are characteristically high dimensional with a small sample size in contrast to the feature size and variability inherent in biological processes that contribute to difficulties in analysis. Selection of highly discriminative features decreases the computational cost and complexity of the classifier and improves its reliability for prediction of a new class of samples. Methods: The present study used hybrid particle swarm optimization and genetic algorithms for gene selection and a fuzzy support vector machine (SVM) as the classifier. Fuzzy logic is used to infer the importance of each sample in the training phase and decrease the outlier sensitivity of the system to increase the ability to generalize the classifier. A decision-tree algorithm was applied to the most frequent genes to develop a set of rules for each type of cancer. This improved the abilities of the algorithm by finding the best parameters for the classifier during the training phase without the need for trial-and-error by the user. The proposed approach was tested on four benchmark gene expression profiles. Results: Good results have been demonstrated for the proposed algorithm. The classification accuracy for leukemia data is 100%, for colon cancer is 96.67% and for breast cancer is 98%. The results show that the best kernel used in training the SVM classifier is the radial basis function. Conclusions: The experimental results show that the proposed algorithm can decrease the dimensionality of the dataset, determine the most informative gene subset, and improve classification accuracy using the optimal parameters of the classifier with no user interface. PMID:29535919

Robust state estimation for uncertain fuzzy bidirectional associative memory networks with time-varying delays

NASA Astrophysics Data System (ADS)

Vadivel, P.; Sakthivel, R.; Mathiyalagan, K.; Arunkumar, A.

2013-09-01

This paper addresses the issue of robust state estimation for a class of fuzzy bidirectional associative memory (BAM) neural networks with time-varying delays and parameter uncertainties. By constructing the Lyapunov-Krasovskii functional, which contains the triple-integral term and using the free-weighting matrix technique, a set of sufficient conditions are derived in terms of linear matrix inequalities (LMIs) to estimate the neuron states through available output measurements such that the dynamics of the estimation error system is robustly asymptotically stable. In particular, we consider a generalized activation function in which the traditional assumptions on the boundedness, monotony and differentiability of the activation functions are removed. More precisely, the design of the state estimator for such BAM neural networks can be obtained by solving some LMIs, which are dependent on the size of the time derivative of the time-varying delays. Finally, a numerical example with simulation result is given to illustrate the obtained theoretical results.

Fuzzy forecasting based on fuzzy-trend logical relationship groups.

PubMed

Chen, Shyi-Ming; Wang, Nai-Yi

2010-10-01

In this paper, we present a new method to predict the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) based on fuzzy-trend logical relationship groups (FTLRGs). The proposed method divides fuzzy logical relationships into FTLRGs based on the trend of adjacent fuzzy sets appearing in the antecedents of fuzzy logical relationships. First, we apply an automatic clustering algorithm to cluster the historical data into intervals of different lengths. Then, we define fuzzy sets based on these intervals of different lengths. Then, the historical data are fuzzified into fuzzy sets to derive fuzzy logical relationships. Then, we divide the fuzzy logical relationships into FTLRGs for forecasting the TAIEX. Moreover, we also apply the proposed method to forecast the enrollments and the inventory demand, respectively. The experimental results show that the proposed method gets higher average forecasting accuracy rates than the existing methods.

BRAIN NETWORKS. Correlated gene expression supports synchronous activity in brain networks.

PubMed

Richiardi, Jonas; Altmann, Andre; Milazzo, Anna-Clare; Chang, Catie; Chakravarty, M Mallar; Banaschewski, Tobias; Barker, Gareth J; Bokde, Arun L W; Bromberg, Uli; Büchel, Christian; Conrod, Patricia; Fauth-Bühler, Mira; Flor, Herta; Frouin, Vincent; Gallinat, Jürgen; Garavan, Hugh; Gowland, Penny; Heinz, Andreas; Lemaître, Hervé; Mann, Karl F; Martinot, Jean-Luc; Nees, Frauke; Paus, Tomáš; Pausova, Zdenka; Rietschel, Marcella; Robbins, Trevor W; Smolka, Michael N; Spanagel, Rainer; Ströhle, Andreas; Schumann, Gunter; Hawrylycz, Mike; Poline, Jean-Baptiste; Greicius, Michael D

2015-06-12

During rest, brain activity is synchronized between different regions widely distributed throughout the brain, forming functional networks. However, the molecular mechanisms supporting functional connectivity remain undefined. We show that functional brain networks defined with resting-state functional magnetic resonance imaging can be recapitulated by using measures of correlated gene expression in a post mortem brain tissue data set. The set of 136 genes we identify is significantly enriched for ion channels. Polymorphisms in this set of genes significantly affect resting-state functional connectivity in a large sample of healthy adolescents. Expression levels of these genes are also significantly associated with axonal connectivity in the mouse. The results provide convergent, multimodal evidence that resting-state functional networks correlate with the orchestrated activity of dozens of genes linked to ion channel activity and synaptic function. Copyright © 2015, American Association for the Advancement of Science.

Multiscale Embedded Gene Co-expression Network Analysis

PubMed Central

Song, Won-Min; Zhang, Bin

2015-01-01

Gene co-expression network analysis has been shown effective in identifying functional co-expressed gene modules associated with complex human diseases. However, existing techniques to construct co-expression networks require some critical prior information such as predefined number of clusters, numerical thresholds for defining co-expression/interaction, or do not naturally reproduce the hallmarks of complex systems such as the scale-free degree distribution of small-worldness. Previously, a graph filtering technique called Planar Maximally Filtered Graph (PMFG) has been applied to many real-world data sets such as financial stock prices and gene expression to extract meaningful and relevant interactions. However, PMFG is not suitable for large-scale genomic data due to several drawbacks, such as the high computation complexity O(|V|3), the presence of false-positives due to the maximal planarity constraint, and the inadequacy of the clustering framework. Here, we developed a new co-expression network analysis framework called Multiscale Embedded Gene Co-expression Network Analysis (MEGENA) by: i) introducing quality control of co-expression similarities, ii) parallelizing embedded network construction, and iii) developing a novel clustering technique to identify multi-scale clustering structures in Planar Filtered Networks (PFNs). We applied MEGENA to a series of simulated data and the gene expression data in breast carcinoma and lung adenocarcinoma from The Cancer Genome Atlas (TCGA). MEGENA showed improved performance over well-established clustering methods and co-expression network construction approaches. MEGENA revealed not only meaningful multi-scale organizations of co-expressed gene clusters but also novel targets in breast carcinoma and lung adenocarcinoma. PMID:26618778

Multiscale Embedded Gene Co-expression Network Analysis.

PubMed

Song, Won-Min; Zhang, Bin

2015-11-01

Gene co-expression network analysis has been shown effective in identifying functional co-expressed gene modules associated with complex human diseases. However, existing techniques to construct co-expression networks require some critical prior information such as predefined number of clusters, numerical thresholds for defining co-expression/interaction, or do not naturally reproduce the hallmarks of complex systems such as the scale-free degree distribution of small-worldness. Previously, a graph filtering technique called Planar Maximally Filtered Graph (PMFG) has been applied to many real-world data sets such as financial stock prices and gene expression to extract meaningful and relevant interactions. However, PMFG is not suitable for large-scale genomic data due to several drawbacks, such as the high computation complexity O(|V|3), the presence of false-positives due to the maximal planarity constraint, and the inadequacy of the clustering framework. Here, we developed a new co-expression network analysis framework called Multiscale Embedded Gene Co-expression Network Analysis (MEGENA) by: i) introducing quality control of co-expression similarities, ii) parallelizing embedded network construction, and iii) developing a novel clustering technique to identify multi-scale clustering structures in Planar Filtered Networks (PFNs). We applied MEGENA to a series of simulated data and the gene expression data in breast carcinoma and lung adenocarcinoma from The Cancer Genome Atlas (TCGA). MEGENA showed improved performance over well-established clustering methods and co-expression network construction approaches. MEGENA revealed not only meaningful multi-scale organizations of co-expressed gene clusters but also novel targets in breast carcinoma and lung adenocarcinoma.

Fuzzy time-series based on Fibonacci sequence for stock price forecasting

NASA Astrophysics Data System (ADS)

Chen, Tai-Liang; Cheng, Ching-Hsue; Jong Teoh, Hia

2007-07-01

Time-series models have been utilized to make reasonably accurate predictions in the areas of stock price movements, academic enrollments, weather, etc. For promoting the forecasting performance of fuzzy time-series models, this paper proposes a new model, which incorporates the concept of the Fibonacci sequence, the framework of Song and Chissom's model and the weighted method of Yu's model. This paper employs a 5-year period TSMC (Taiwan Semiconductor Manufacturing Company) stock price data and a 13-year period of TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock Index) stock index data as experimental datasets. By comparing our forecasting performances with Chen's (Forecasting enrollments based on fuzzy time-series. Fuzzy Sets Syst. 81 (1996) 311-319), Yu's (Weighted fuzzy time-series models for TAIEX forecasting. Physica A 349 (2004) 609-624) and Huarng's (The application of neural networks to forecast fuzzy time series. Physica A 336 (2006) 481-491) models, we conclude that the proposed model surpasses in accuracy these conventional fuzzy time-series models.

A reinforcement learning-based architecture for fuzzy logic control

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.

1992-01-01

This paper introduces a new method for learning to refine a rule-based fuzzy logic controller. A reinforcement learning technique is used in conjunction with a multilayer neural network model of a fuzzy controller. The approximate reasoning based intelligent control (ARIC) architecture proposed here learns by updating its prediction of the physical system's behavior and fine tunes a control knowledge base. Its theory is related to Sutton's temporal difference (TD) method. Because ARIC has the advantage of using the control knowledge of an experienced operator and fine tuning it through the process of learning, it learns faster than systems that train networks from scratch. The approach is applied to a cart-pole balancing system.

Inferring gene regression networks with model trees

PubMed Central

2010-01-01

Background Novel strategies are required in order to handle the huge amount of data produced by microarray technologies. To infer gene regulatory networks, the first step is to find direct regulatory relationships between genes building the so-called gene co-expression networks. They are typically generated using correlation statistics as pairwise similarity measures. Correlation-based methods are very useful in order to determine whether two genes have a strong global similarity but do not detect local similarities. Results We propose model trees as a method to identify gene interaction networks. While correlation-based methods analyze each pair of genes, in our approach we generate a single regression tree for each gene from the remaining genes. Finally, a graph from all the relationships among output and input genes is built taking into account whether the pair of genes is statistically significant. For this reason we apply a statistical procedure to control the false discovery rate. The performance of our approach, named REGNET, is experimentally tested on two well-known data sets: Saccharomyces Cerevisiae and E.coli data set. First, the biological coherence of the results are tested. Second the E.coli transcriptional network (in the Regulon database) is used as control to compare the results to that of a correlation-based method. This experiment shows that REGNET performs more accurately at detecting true gene associations than the Pearson and Spearman zeroth and first-order correlation-based methods. Conclusions REGNET generates gene association networks from gene expression data, and differs from correlation-based methods in that the relationship between one gene and others is calculated simultaneously. Model trees are very useful techniques to estimate the numerical values for the target genes by linear regression functions. They are very often more precise than linear regression models because they can add just different linear regressions to separate

Hybrid neural network and fuzzy logic approaches for rendezvous and capture in space

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.; Castellano, Timothy

1991-01-01

The nonlinear behavior of many practical systems and unavailability of quantitative data regarding the input-output relations makes the analytical modeling of these systems very difficult. On the other hand, approximate reasoning-based controllers which do not require analytical models have demonstrated a number of successful applications such as the subway system in the city of Sendai. These applications have mainly concentrated on emulating the performance of a skilled human operator in the form of linguistic rules. However, the process of learning and tuning the control rules to achieve the desired performance remains a difficult task. Fuzzy Logic Control is based on fuzzy set theory. A fuzzy set is an extension of a crisp set. Crisp sets only allow full membership or no membership at all, whereas fuzzy sets allow partial membership. In other words, an element may partially belong to a set.

Gene network polymorphism is the raw material of natural selection: the selfish gene network hypothesis.

PubMed

Boldogköi, Zsolt

2004-09-01

Population genetics, the mathematical theory of modern evolutionary biology, defines evolution as the alteration of the frequency of distinct gene variants (alleles) differing in fitness over the time. The major problem with this view is that in gene and protein sequences we can find little evidence concerning the molecular basis of phenotypic variance, especially those that would confer adaptive benefit to the bearers. Some novel data, however, suggest that a large amount of genetic variation exists in the regulatory region of genes within populations. In addition, comparison of homologous DNA sequences of various species shows that evolution appears to depend more strongly on gene expression than on the genes themselves. Furthermore, it has been demonstrated in several systems that genes form functional networks, whose products exhibit interrelated expression profiles. Finally, it has been found that regulatory circuits of development behave as evolutionary units. These data demonstrate that our view of evolution calls for a new synthesis. In this article I propose a novel concept, termed the selfish gene network hypothesis, which is based on an overall consideration of the above findings. The major statements of this hypothesis are as follows. (1) Instead of individual genes, gene networks (GNs) are responsible for the determination of traits and behaviors. (2) The primary source of microevolution is the intraspecific polymorphism in GNs and not the allelic variation in either the coding or the regulatory sequences of individual genes. (3) GN polymorphism is generated by the variation in the regulatory regions of the component genes and not by the variance in their coding sequences. (4) Evolution proceeds through continuous restructuring of the composition of GNs rather than fixing of specific alleles or GN variants.

Genes and gene networks implicated in aggression related behaviour.

PubMed

Malki, Karim; Pain, Oliver; Du Rietz, Ebba; Tosto, Maria Grazia; Paya-Cano, Jose; Sandnabba, Kenneth N; de Boer, Sietse; Schalkwyk, Leonard C; Sluyter, Frans

2014-10-01

Aggressive behaviour is a major cause of mortality and morbidity. Despite of moderate heritability estimates, progress in identifying the genetic factors underlying aggressive behaviour has been limited. There are currently three genetic mouse models of high and low aggression created using selective breeding. This is the first study to offer a global transcriptomic characterization of the prefrontal cortex across all three genetic mouse models of aggression. A systems biology approach has been applied to transcriptomic data across the three pairs of selected inbred mouse strains (Turku Aggressive (TA) and Turku Non-Aggressive (TNA), Short Attack Latency (SAL) and Long Attack Latency (LAL) mice and North Carolina Aggressive (NC900) and North Carolina Non-Aggressive (NC100)), providing novel insight into the neurobiological mechanisms and genetics underlying aggression. First, weighted gene co-expression network analysis (WGCNA) was performed to identify modules of highly correlated genes associated with aggression. Probe sets belonging to gene modules uncovered by WGCNA were carried forward for network analysis using ingenuity pathway analysis (IPA). The RankProd non-parametric algorithm was then used to statistically evaluate expression differences across the genes belonging to modules significantly associated with aggression. IPA uncovered two pathways, involving NF-kB and MAPKs. The secondary RankProd analysis yielded 14 differentially expressed genes, some of which have previously been implicated in pathways associated with aggressive behaviour, such as Adrbk2. The results highlighted plausible candidate genes and gene networks implicated in aggression-related behaviour.

Knowledge-guided fuzzy logic modeling to infer cellular signaling networks from proteomic data

PubMed Central

Liu, Hui; Zhang, Fan; Mishra, Shital Kumar; Zhou, Shuigeng; Zheng, Jie

2016-01-01

Modeling of signaling pathways is crucial for understanding and predicting cellular responses to drug treatments. However, canonical signaling pathways curated from literature are seldom context-specific and thus can hardly predict cell type-specific response to external perturbations; purely data-driven methods also have drawbacks such as limited biological interpretability. Therefore, hybrid methods that can integrate prior knowledge and real data for network inference are highly desirable. In this paper, we propose a knowledge-guided fuzzy logic network model to infer signaling pathways by exploiting both prior knowledge and time-series data. In particular, the dynamic time warping algorithm is employed to measure the goodness of fit between experimental and predicted data, so that our method can model temporally-ordered experimental observations. We evaluated the proposed method on a synthetic dataset and two real phosphoproteomic datasets. The experimental results demonstrate that our model can uncover drug-induced alterations in signaling pathways in cancer cells. Compared with existing hybrid models, our method can model feedback loops so that the dynamical mechanisms of signaling networks can be uncovered from time-series data. By calibrating generic models of signaling pathways against real data, our method supports precise predictions of context-specific anticancer drug effects, which is an important step towards precision medicine. PMID:27774993

The architecture of adaptive neural network based on a fuzzy inference system for implementing intelligent control in photovoltaic systems

NASA Astrophysics Data System (ADS)

Gimazov, R.; Shidlovskiy, S.

2018-05-01

In this paper, we consider the architecture of the algorithm for extreme regulation in the photovoltaic system. An algorithm based on an adaptive neural network with fuzzy inference is proposed. The implementation of such an algorithm not only allows solving a number of problems in existing algorithms for extreme power regulation of photovoltaic systems, but also creates a reserve for the creation of a universal control system for a photovoltaic system.

Learning and tuning fuzzy logic controllers through reinforcements.

PubMed

Berenji, H R; Khedkar, P

1992-01-01

A method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system is presented. It is shown that: the generalized approximate-reasoning-based intelligent control (GARIC) architecture learns and tunes a fuzzy logic controller even when only weak reinforcement, such as a binary failure signal, is available; introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward network, which can then adaptively improve performance by using gradient descent methods. The GARIC architecture is applied to a cart-pole balancing system and demonstrates significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing.

Extending gene ontology with gene association networks.

PubMed

Peng, Jiajie; Wang, Tao; Wang, Jixuan; Wang, Yadong; Chen, Jin

2016-04-15

Gene ontology (GO) is a widely used resource to describe the attributes for gene products. However, automatic GO maintenance remains to be difficult because of the complex logical reasoning and the need of biological knowledge that are not explicitly represented in the GO. The existing studies either construct whole GO based on network data or only infer the relations between existing GO terms. None is purposed to add new terms automatically to the existing GO. We proposed a new algorithm 'GOExtender' to efficiently identify all the connected gene pairs labeled by the same parent GO terms. GOExtender is used to predict new GO terms with biological network data, and connect them to the existing GO. Evaluation tests on biological process and cellular component categories of different GO releases showed that GOExtender can extend new GO terms automatically based on the biological network. Furthermore, we applied GOExtender to the recent release of GO and discovered new GO terms with strong support from literature. Software and supplementary document are available at www.msu.edu/%7Ejinchen/GOExtender jinchen@msu.edu or ydwang@hit.edu.cn Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

HyFIS: adaptive neuro-fuzzy inference systems and their application to nonlinear dynamical systems.

PubMed

Kim, J; Kasabov, N

1999-11-01

This paper proposes an adaptive neuro-fuzzy system, HyFIS (Hybrid neural Fuzzy Inference System), for building and optimising fuzzy models. The proposed model introduces the learning power of neural networks to fuzzy logic systems and provides linguistic meaning to the connectionist architectures. Heuristic fuzzy logic rules and input-output fuzzy membership functions can be optimally tuned from training examples by a hybrid learning scheme comprised of two phases: rule generation phase from data; and rule tuning phase using error backpropagation learning scheme for a neural fuzzy system. To illustrate the performance and applicability of the proposed neuro-fuzzy hybrid model, extensive simulation studies of nonlinear complex dynamic systems are carried out. The proposed method can be applied to an on-line incremental adaptive learning for the prediction and control of nonlinear dynamical systems. Two benchmark case studies are used to demonstrate that the proposed HyFIS system is a superior neuro-fuzzy modelling technique.

Fuzzy Logic Engine

NASA Technical Reports Server (NTRS)

Howard, Ayanna

2005-01-01

The Fuzzy Logic Engine is a software package that enables users to embed fuzzy-logic modules into their application programs. Fuzzy logic is useful as a means of formulating human expert knowledge and translating it into software to solve problems. Fuzzy logic provides flexibility for modeling relationships between input and output information and is distinguished by its robustness with respect to noise and variations in system parameters. In addition, linguistic fuzzy sets and conditional statements allow systems to make decisions based on imprecise and incomplete information. The user of the Fuzzy Logic Engine need not be an expert in fuzzy logic: it suffices to have a basic understanding of how linguistic rules can be applied to the user's problem. The Fuzzy Logic Engine is divided into two modules: (1) a graphical-interface software tool for creating linguistic fuzzy sets and conditional statements and (2) a fuzzy-logic software library for embedding fuzzy processing capability into current application programs. The graphical- interface tool was developed using the Tcl/Tk programming language. The fuzzy-logic software library was written in the C programming language.

Functional Module Analysis for Gene Coexpression Networks with Network Integration.

PubMed

Zhang, Shuqin; Zhao, Hongyu; Ng, Michael K

2015-01-01

Network has been a general tool for studying the complex interactions between different genes, proteins, and other small molecules. Module as a fundamental property of many biological networks has been widely studied and many computational methods have been proposed to identify the modules in an individual network. However, in many cases, a single network is insufficient for module analysis due to the noise in the data or the tuning of parameters when building the biological network. The availability of a large amount of biological networks makes network integration study possible. By integrating such networks, more informative modules for some specific disease can be derived from the networks constructed from different tissues, and consistent factors for different diseases can be inferred. In this paper, we have developed an effective method for module identification from multiple networks under different conditions. The problem is formulated as an optimization model, which combines the module identification in each individual network and alignment of the modules from different networks together. An approximation algorithm based on eigenvector computation is proposed. Our method outperforms the existing methods, especially when the underlying modules in multiple networks are different in simulation studies. We also applied our method to two groups of gene coexpression networks for humans, which include one for three different cancers, and one for three tissues from the morbidly obese patients. We identified 13 modules with three complete subgraphs, and 11 modules with two complete subgraphs, respectively. The modules were validated through Gene Ontology enrichment and KEGG pathway enrichment analysis. We also showed that the main functions of most modules for the corresponding disease have been addressed by other researchers, which may provide the theoretical basis for further studying the modules experimentally.

Neurocontrol and fuzzy logic: Connections and designs

NASA Technical Reports Server (NTRS)

Werbos, Paul J.

1991-01-01

Artificial neural networks (ANNs) and fuzzy logic are complementary technologies. ANNs extract information from systems to be learned or controlled, while fuzzy techniques mainly use verbal information from experts. Ideally, both sources of information should be combined. For example, one can learn rules in a hybrid fashion, and then calibrate them for better whole-system performance. ANNs offer universal approximation theorems, pedagogical advantages, very high-throughput hardware, and links to neurophysiology. Neurocontrol - the use of ANNs to directly control motors or actuators, etc. - uses five generalized designs, related to control theory, which can work on fuzzy logic systems as well as ANNs. These designs can copy what experts do instead of what they say, learn to track trajectories, generalize adaptive control, and maximize performance or minimize cost over time, even in noisy environments. Design tradeoffs and future directions are discussed throughout.

Decomposed fuzzy systems and their application in direct adaptive fuzzy control.

PubMed

Hsueh, Yao-Chu; Su, Shun-Feng; Chen, Ming-Chang

2014-10-01

In this paper, a novel fuzzy structure termed as the decomposed fuzzy system (DFS) is proposed to act as the fuzzy approximator for adaptive fuzzy control systems. The proposed structure is to decompose each fuzzy variable into layers of fuzzy systems, and each layer is to characterize one traditional fuzzy set. Similar to forming fuzzy rules in traditional fuzzy systems, layers from different variables form the so-called component fuzzy systems. DFS is proposed to provide more adjustable parameters to facilitate possible adaptation in fuzzy rules, but without introducing a learning burden. It is because those component fuzzy systems are independent so that it can facilitate minimum distribution learning effects among component fuzzy systems. It can be seen from our experiments that even when the rule number increases, the learning time in terms of cycles is still almost constant. It can also be found that the function approximation capability and learning efficiency of the DFS are much better than that of the traditional fuzzy systems when employed in adaptive fuzzy control systems. Besides, in order to further reduce the computational burden, a simplified DFS is proposed in this paper to satisfy possible real time constraints required in many applications. From our simulation results, it can be seen that the simplified DFS can perform fairly with a more concise decomposition structure.

Towards a Fuzzy Bayesian Network Based Approach for Safety Risk Analysis of Tunnel-Induced Pipeline Damage.

PubMed

Zhang, Limao; Wu, Xianguo; Qin, Yawei; Skibniewski, Miroslaw J; Liu, Wenli

2016-02-01

Tunneling excavation is bound to produce significant disturbances to surrounding environments, and the tunnel-induced damage to adjacent underground buried pipelines is of considerable importance for geotechnical practice. A fuzzy Bayesian networks (FBNs) based approach for safety risk analysis is developed in this article with detailed step-by-step procedures, consisting of risk mechanism analysis, the FBN model establishment, fuzzification, FBN-based inference, defuzzification, and decision making. In accordance with the failure mechanism analysis, a tunnel-induced pipeline damage model is proposed to reveal the cause-effect relationships between the pipeline damage and its influential variables. In terms of the fuzzification process, an expert confidence indicator is proposed to reveal the reliability of the data when determining the fuzzy probability of occurrence of basic events, with both the judgment ability level and the subjectivity reliability level taken into account. By means of the fuzzy Bayesian inference, the approach proposed in this article is capable of calculating the probability distribution of potential safety risks and identifying the most likely potential causes of accidents under both prior knowledge and given evidence circumstances. A case concerning the safety analysis of underground buried pipelines adjacent to the construction of the Wuhan Yangtze River Tunnel is presented. The results demonstrate the feasibility of the proposed FBN approach and its application potential. The proposed approach can be used as a decision tool to provide support for safety assurance and management in tunnel construction, and thus increase the likelihood of a successful project in a complex project environment. © 2015 Society for Risk Analysis.

Using a fuzzy comprehensive evaluation method to determine product usability: A test case.

PubMed

Zhou, Ronggang; Chan, Alan H S

2017-01-01

In order to take into account the inherent uncertainties during product usability evaluation, Zhou and Chan [1] proposed a comprehensive method of usability evaluation for products by combining the analytic hierarchy process (AHP) and fuzzy evaluation methods for synthesizing performance data and subjective response data. This method was designed to provide an integrated framework combining the inevitable vague judgments from the multiple stages of the product evaluation process. In order to illustrate the effectiveness of the model, this study used a summative usability test case to assess the application and strength of the general fuzzy usability framework. To test the proposed fuzzy usability evaluation framework [1], a standard summative usability test was conducted to benchmark the overall usability of a specific network management software. Based on the test data, the fuzzy method was applied to incorporate both the usability scores and uncertainties involved in the multiple components of the evaluation. Then, with Monte Carlo simulation procedures, confidence intervals were used to compare the reliabilities among the fuzzy approach and two typical conventional methods combining metrics based on percentages. This case study showed that the fuzzy evaluation technique can be applied successfully for combining summative usability testing data to achieve an overall usability quality for the network software evaluated. Greater differences of confidence interval widths between the method of averaging equally percentage and weighted evaluation method, including the method of weighted percentage averages, verified the strength of the fuzzy method.

Disease gene prioritization by integrating tissue-specific molecular networks using a robust multi-network model.

PubMed

Ni, Jingchao; Koyuturk, Mehmet; Tong, Hanghang; Haines, Jonathan; Xu, Rong; Zhang, Xiang

2016-11-10

Accurately prioritizing candidate disease genes is an important and challenging problem. Various network-based methods have been developed to predict potential disease genes by utilizing the disease similarity network and molecular networks such as protein interaction or gene co-expression networks. Although successful, a common limitation of the existing methods is that they assume all diseases share the same molecular network and a single generic molecular network is used to predict candidate genes for all diseases. However, different diseases tend to manifest in different tissues, and the molecular networks in different tissues are usually different. An ideal method should be able to incorporate tissue-specific molecular networks for different diseases. In this paper, we develop a robust and flexible method to integrate tissue-specific molecular networks for disease gene prioritization. Our method allows each disease to have its own tissue-specific network(s). We formulate the problem of candidate gene prioritization as an optimization problem based on network propagation. When there are multiple tissue-specific networks available for a disease, our method can automatically infer the relative importance of each tissue-specific network. Thus it is robust to the noisy and incomplete network data. To solve the optimization problem, we develop fast algorithms which have linear time complexities in the number of nodes in the molecular networks. We also provide rigorous theoretical foundations for our algorithms in terms of their optimality and convergence properties. Extensive experimental results show that our method can significantly improve the accuracy of candidate gene prioritization compared with the state-of-the-art methods. In our experiments, we compare our methods with 7 popular network-based disease gene prioritization algorithms on diseases from Online Mendelian Inheritance in Man (OMIM) database. The experimental results demonstrate that our methods

Fuzzy Kernel k-Medoids algorithm for anomaly detection problems

NASA Astrophysics Data System (ADS)

Rustam, Z.; Talita, A. S.

2017-07-01

Intrusion Detection System (IDS) is an essential part of security systems to strengthen the security of information systems. IDS can be used to detect the abuse by intruders who try to get into the network system in order to access and utilize the available data sources in the system. There are two approaches of IDS, Misuse Detection and Anomaly Detection (behavior-based intrusion detection). Fuzzy clustering-based methods have been widely used to solve Anomaly Detection problems. Other than using fuzzy membership concept to determine the object to a cluster, other approaches as in combining fuzzy and possibilistic membership or feature-weighted based methods are also used. We propose Fuzzy Kernel k-Medoids that combining fuzzy and possibilistic membership as a powerful method to solve anomaly detection problem since on numerical experiment it is able to classify IDS benchmark data into five different classes simultaneously. We classify IDS benchmark data KDDCup'99 data set into five different classes simultaneously with the best performance was achieved by using 30 % of training data with clustering accuracy reached 90.28 percent.

Energy efficient wireless sensor networks by using a fuzzy-based solution

NASA Astrophysics Data System (ADS)

Tirrito, Salvatore; Nicolosi, Giuseppina

2016-12-01

Wireless Sensor Networks are characterized by a distributed architecture realized by a set of autonomous electronic devices able to sense data from the surrounding environment and to communicate among them. These devices are battery powered since they may be used even to monitor hazardous events in inaccessible areas. As a consequence, it is preferable to assure the adoption of energy management solutions in order to extend the WSN lifetime, as far as possible. Moreover, it is crucial to guarantee that the nodes receive the transmitted data correctly. It is clear that trading off power optimization and quality of service has become one the most important concerns when dealing with modern systems based on WSNs. This paper introduces a solution based on a Fuzzy Logic Controller (FLC) focusing on the minimization of energy consumption of wireless sensor nodes. This is made possible because the sleeping time of these nodes is dynamically regulated by a FLC.

Fine-tuning gene networks using simple sequence repeats

PubMed Central

Egbert, Robert G.; Klavins, Eric

2012-01-01

The parameters in a complex synthetic gene network must be extensively tuned before the network functions as designed. Here, we introduce a simple and general approach to rapidly tune gene networks in Escherichia coli using hypermutable simple sequence repeats embedded in the spacer region of the ribosome binding site. By varying repeat length, we generated expression libraries that incrementally and predictably sample gene expression levels over a 1,000-fold range. We demonstrate the utility of the approach by creating a bistable switch library that programmatically samples the expression space to balance the two states of the switch, and we illustrate the need for tuning by showing that the switch’s behavior is sensitive to host context. Further, we show that mutation rates of the repeats are controllable in vivo for stability or for targeted mutagenesis—suggesting a new approach to optimizing gene networks via directed evolution. This tuning methodology should accelerate the process of engineering functionally complex gene networks. PMID:22927382

Efficient Reverse-Engineering of a Developmental Gene Regulatory Network

PubMed Central

Cicin-Sain, Damjan; Ashyraliyev, Maksat; Jaeger, Johannes

2012-01-01

Understanding the complex regulatory networks underlying development and evolution of multi-cellular organisms is a major problem in biology. Computational models can be used as tools to extract the regulatory structure and dynamics of such networks from gene expression data. This approach is called reverse engineering. It has been successfully applied to many gene networks in various biological systems. However, to reconstitute the structure and non-linear dynamics of a developmental gene network in its spatial context remains a considerable challenge. Here, we address this challenge using a case study: the gap gene network involved in segment determination during early development of Drosophila melanogaster. A major problem for reverse-engineering pattern-forming networks is the significant amount of time and effort required to acquire and quantify spatial gene expression data. We have developed a simplified data processing pipeline that considerably increases the throughput of the method, but results in data of reduced accuracy compared to those previously used for gap gene network inference. We demonstrate that we can infer the correct network structure using our reduced data set, and investigate minimal data requirements for successful reverse engineering. Our results show that timing and position of expression domain boundaries are the crucial features for determining regulatory network structure from data, while it is less important to precisely measure expression levels. Based on this, we define minimal data requirements for gap gene network inference. Our results demonstrate the feasibility of reverse-engineering with much reduced experimental effort. This enables more widespread use of the method in different developmental contexts and organisms. Such systematic application of data-driven models to real-world networks has enormous potential. Only the quantitative investigation of a large number of developmental gene regulatory networks will allow us to

MyGeneFriends: A Social Network Linking Genes, Genetic Diseases, and Researchers

PubMed Central

Allot, Alexis; Chennen, Kirsley; Nevers, Yannis; Poidevin, Laetitia; Kress, Arnaud; Ripp, Raymond; Thompson, Julie Dawn; Poch, Olivier

2017-01-01

Background The constant and massive increase of biological data offers unprecedented opportunities to decipher the function and evolution of genes and their roles in human diseases. However, the multiplicity of sources and flow of data mean that efficient access to useful information and knowledge production has become a major challenge. This challenge can be addressed by taking inspiration from Web 2.0 and particularly social networks, which are at the forefront of big data exploration and human-data interaction. Objective MyGeneFriends is a Web platform inspired by social networks, devoted to genetic disease analysis, and organized around three types of proactive agents: genes, humans, and genetic diseases. The aim of this study was to improve exploration and exploitation of biological, postgenomic era big data. Methods MyGeneFriends leverages conventions popularized by top social networks (Facebook, LinkedIn, etc), such as networks of friends, profile pages, friendship recommendations, affinity scores, news feeds, content recommendation, and data visualization. Results MyGeneFriends provides simple and intuitive interactions with data through evaluation and visualization of connections (friendships) between genes, humans, and diseases. The platform suggests new friends and publications and allows agents to follow the activity of their friends. It dynamically personalizes information depending on the user’s specific interests and provides an efficient way to share information with collaborators. Furthermore, the user’s behavior itself generates new information that constitutes an added value integrated in the network, which can be used to discover new connections between biological agents. Conclusions We have developed MyGeneFriends, a Web platform leveraging conventions from popular social networks to redefine the relationship between humans and biological big data and improve human processing of biomedical data. MyGeneFriends is available at lbgi

Fuzzy Versions of Epistemic and Deontic Logic

NASA Technical Reports Server (NTRS)

Gounder, Ramasamy S.; Esterline, Albert C.

1998-01-01

Epistemic and deontic logics are modal logics, respectively, of knowledge and of the normative concepts of obligation, permission, and prohibition. Epistemic logic is useful in formalizing systems of communicating processes and knowledge and belief in AI (Artificial Intelligence). Deontic logic is useful in computer science wherever we must distinguish between actual and ideal behavior, as in fault tolerance and database integrity constraints. We here discuss fuzzy versions of these logics. In the crisp versions, various axioms correspond to various properties of the structures used in defining the semantics of the logics. Thus, any axiomatic theory will be characterized not only by its axioms but also by the set of properties holding of the corresponding semantic structures. Fuzzy logic does not proceed with axiomatic systems, but fuzzy versions of the semantic properties exist and can be shown to correspond to some of the axioms for the crisp systems in special ways that support dependency networks among assertions in a modal domain. This in turn allows one to implement truth maintenance systems. For the technical development of epistemic logic, and for that of deontic logic. To our knowledge, we are the first to address fuzzy epistemic and fuzzy deontic logic explicitly and to consider the different systems and semantic properties available. We give the syntax and semantics of epistemic logic and discuss the correspondence between axioms of epistemic logic and properties of semantic structures. The same topics are covered for deontic logic. Fuzzy epistemic and fuzzy deontic logic discusses the relationship between axioms and semantic properties for these logics. Our results can be exploited in truth maintenance systems.

Dynamic cluster generation for a fuzzy classifier with ellipsoidal regions.

PubMed

Abe, S

1998-01-01

In this paper, we discuss a fuzzy classifier with ellipsoidal regions that dynamically generates clusters. First, for the data belonging to a class we define a fuzzy rule with an ellipsoidal region. Namely, using the training data for each class, we calculate the center and the covariance matrix of the ellipsoidal region for the class. Then we tune the fuzzy rules, i.e., the slopes of the membership functions, successively until there is no improvement in the recognition rate of the training data. Then if the number of the data belonging to a class that are misclassified into another class exceeds a prescribed number, we define a new cluster to which those data belong and the associated fuzzy rule. Then we tune the newly defined fuzzy rules in the similar way as stated above, fixing the already obtained fuzzy rules. We iterate generation of clusters and tuning of the newly generated fuzzy rules until the number of the data belonging to a class that are misclassified into another class does not exceed the prescribed number. We evaluate our method using thyroid data, Japanese Hiragana data of vehicle license plates, and blood cell data. By dynamic cluster generation, the generalization ability of the classifier is improved and the recognition rate of the fuzzy classifier for the test data is the best among the neural network classifiers and other fuzzy classifiers if there are no discrete input variables.

Trainable Gene Regulation Networks with Applications to Drosophila Pattern Formation

NASA Technical Reports Server (NTRS)

Mjolsness, Eric

2000-01-01

This chapter will very briefly introduce and review some computational experiments in using trainable gene regulation network models to simulate and understand selected episodes in the development of the fruit fly, Drosophila melanogaster. For details the reader is referred to the papers introduced below. It will then introduce a new gene regulation network model which can describe promoter-level substructure in gene regulation. As described in chapter 2, gene regulation may be thought of as a combination of cis-acting regulation by the extended promoter of a gene (including all regulatory sequences) by way of the transcription complex, and of trans-acting regulation by the transcription factor products of other genes. If we simplify the cis-action by using a phenomenological model which can be tuned to data, such as a unit or other small portion of an artificial neural network, then the full transacting interaction between multiple genes during development can be modelled as a larger network which can again be tuned or trained to data. The larger network will in general need to have recurrent (feedback) connections since at least some real gene regulation networks do. This is the basic modeling approach taken, which describes how a set of recurrent neural networks can be used as a modeling language for multiple developmental processes including gene regulation within a single cell, cell-cell communication, and cell division. Such network models have been called "gene circuits", "gene regulation networks", or "genetic regulatory networks", sometimes without distinguishing the models from the actual modeled systems.

Fuzzy wavelet plus a quantum neural network as a design base for power system stability enhancement.

PubMed

Ganjefar, Soheil; Tofighi, Morteza; Karami, Hamidreza

2015-11-01

In this study, we introduce an indirect adaptive fuzzy wavelet neural controller (IAFWNC) as a power system stabilizer to damp inter-area modes of oscillations in a multi-machine power system. Quantum computing is an efficient method for improving the computational efficiency of neural networks, so we developed an identifier based on a quantum neural network (QNN) to train the IAFWNC in the proposed scheme. All of the controller parameters are tuned online based on the Lyapunov stability theory to guarantee the closed-loop stability. A two-machine, two-area power system equipped with a static synchronous series compensator as a series flexible ac transmission system was used to demonstrate the effectiveness of the proposed controller. The simulation and experimental results demonstrated that the proposed IAFWNC scheme can achieve favorable control performance. Copyright © 2015 Elsevier Ltd. All rights reserved.

Using protein-protein interactions for refining gene networks estimated from microarray data by Bayesian networks.

PubMed

Nariai, N; Kim, S; Imoto, S; Miyano, S

2004-01-01

We propose a statistical method to estimate gene networks from DNA microarray data and protein-protein interactions. Because physical interactions between proteins or multiprotein complexes are likely to regulate biological processes, using only mRNA expression data is not sufficient for estimating a gene network accurately. Our method adds knowledge about protein-protein interactions to the estimation method of gene networks under a Bayesian statistical framework. In the estimated gene network, a protein complex is modeled as a virtual node based on principal component analysis. We show the effectiveness of the proposed method through the analysis of Saccharomyces cerevisiae cell cycle data. The proposed method improves the accuracy of the estimated gene networks, and successfully identifies some biological facts.

Fuzzy-logic-based network for complex systems risk assessment: application to ship performance analysis.

PubMed

Abou, Seraphin C

2012-03-01

In this paper, a new interpretation of intuitionistic fuzzy sets in the advanced framework of the Dempster-Shafer theory of evidence is extended to monitor safety-critical systems' performance. Not only is the proposed approach more effective, but it also takes into account the fuzzy rules that deal with imperfect knowledge/information and, therefore, is different from the classical Takagi-Sugeno fuzzy system, which assumes that the rule (the knowledge) is perfect. We provide an analytical solution to the practical and important problem of the conceptual probabilistic approach for formal ship safety assessment using the fuzzy set theory that involves uncertainties associated with the reliability input data. Thus, the overall safety of the ship engine is investigated as an object of risk analysis using the fuzzy mapping structure, which considers uncertainty and partial truth in the input-output mapping. The proposed method integrates direct evidence of the frame of discernment and is demonstrated through references to examples where fuzzy set models are informative. These simple applications illustrate how to assess the conflict of sensor information fusion for a sufficient cooling power system of vessels under extreme operation conditions. It was found that propulsion engine safety systems are not only a function of many environmental and operation profiles but are also dynamic and complex. Copyright © 2011 Elsevier Ltd. All rights reserved.

Fuzzy scalar and vector median filters based on fuzzy distances.

PubMed

Chatzis, V; Pitas, I

1999-01-01

In this paper, the fuzzy scalar median (FSM) is proposed, defined by using ordering of fuzzy numbers based on fuzzy minimum and maximum operations defined by using the extension principle. Alternatively, the FSM is defined from the minimization of a fuzzy distance measure, and the equivalence of the two definitions is proven. Then, the fuzzy vector median (FVM) is proposed as an extension of vector median, based on a novel distance definition of fuzzy vectors, which satisfy the property of angle decomposition. By defining properly the fuzziness of a value, the combination of the basic properties of the classical scalar and vector median (VM) filter with other desirable characteristics can be succeeded.

Complex Fuzzy Set-Valued Complex Fuzzy Measures and Their Properties

PubMed Central

Ma, Shengquan; Li, Shenggang

2014-01-01

Let F*(K) be the set of all fuzzy complex numbers. In this paper some classical and measure-theoretical notions are extended to the case of complex fuzzy sets. They are fuzzy complex number-valued distance on F*(K), fuzzy complex number-valued measure on F*(K), and some related notions, such as null-additivity, pseudo-null-additivity, null-subtraction, pseudo-null-subtraction, autocontionuous from above, autocontionuous from below, and autocontinuity of the defined fuzzy complex number-valued measures. Properties of fuzzy complex number-valued measures are studied in detail. PMID:25093202

Prediction of Breeding Values for Dairy Cattle Using Artificial Neural Networks and Neuro-Fuzzy Systems

PubMed Central

Shahinfar, Saleh; Mehrabani-Yeganeh, Hassan; Lucas, Caro; Kalhor, Ahmad; Kazemian, Majid; Weigel, Kent A.

2012-01-01

Developing machine learning and soft computing techniques has provided many opportunities for researchers to establish new analytical methods in different areas of science. The objective of this study is to investigate the potential of two types of intelligent learning methods, artificial neural networks and neuro-fuzzy systems, in order to estimate breeding values (EBV) of Iranian dairy cattle. Initially, the breeding values of lactating Holstein cows for milk and fat yield were estimated using conventional best linear unbiased prediction (BLUP) with an animal model. Once that was established, a multilayer perceptron was used to build ANN to predict breeding values from the performance data of selection candidates. Subsequently, fuzzy logic was used to form an NFS, a hybrid intelligent system that was implemented via a local linear model tree algorithm. For milk yield the correlations between EBV and EBV predicted by the ANN and NFS were 0.92 and 0.93, respectively. Corresponding correlations for fat yield were 0.93 and 0.93, respectively. Correlations between multitrait predictions of EBVs for milk and fat yield when predicted simultaneously by ANN were 0.93 and 0.93, respectively, whereas corresponding correlations with reference EBV for multitrait NFS were 0.94 and 0.95, respectively, for milk and fat production. PMID:22991575

Measuring Distance of Fuzzy Numbers by Trapezoidal Fuzzy Numbers

NASA Astrophysics Data System (ADS)

Hajjari, Tayebeh

2010-11-01

Fuzzy numbers and more generally linguistic values are approximate assessments, given by experts and accepted by decision-makers when obtaining value that is more accurate is impossible or unnecessary. Distance between two fuzzy numbers plays an important role in linguistic decision-making. It is reasonable to define a fuzzy distance between fuzzy objects. To achieve this aim, the researcher presents a new distance measure for fuzzy numbers by means of improved centroid distance method. The metric properties are also studied. The advantage is the calculation of the proposed method is far simple than previous approaches.

Fuzzy portfolio model with fuzzy-input return rates and fuzzy-output proportions

NASA Astrophysics Data System (ADS)

Tsaur, Ruey-Chyn

2015-02-01

In the finance market, a short-term investment strategy is usually applied in portfolio selection in order to reduce investment risk; however, the economy is uncertain and the investment period is short. Further, an investor has incomplete information for selecting a portfolio with crisp proportions for each chosen security. In this paper we present a new method of constructing fuzzy portfolio model for the parameters of fuzzy-input return rates and fuzzy-output proportions, based on possibilistic mean-standard deviation models. Furthermore, we consider both excess or shortage of investment in different economic periods by using fuzzy constraint for the sum of the fuzzy proportions, and we also refer to risks of securities investment and vagueness of incomplete information during the period of depression economics for the portfolio selection. Finally, we present a numerical example of a portfolio selection problem to illustrate the proposed model and a sensitivity analysis is realised based on the results.

Combining fuzzy mathematics with fuzzy logic to solve business management problems

NASA Astrophysics Data System (ADS)

Vrba, Joseph A.

1993-12-01

Fuzzy logic technology has been applied to control problems with great success. Because of this, many observers fell that fuzzy logic is applicable only in the control arena. However, business management problems almost never deal with crisp values. Fuzzy systems technology--a combination of fuzzy logic, fuzzy mathematics and a graphical user interface--is a natural fit for developing software to assist in typical business activities such as planning, modeling and estimating. This presentation discusses how fuzzy logic systems can be extended through the application of fuzzy mathematics and the use of a graphical user interface to make the information contained in fuzzy numbers accessible to business managers. As demonstrated through examples from actual deployed systems, this fuzzy systems technology has been employed successfully to provide solutions to the complex real-world problems found in the business environment.

Gene function prediction with gene interaction networks: a context graph kernel approach.

PubMed

Li, Xin; Chen, Hsinchun; Li, Jiexun; Zhang, Zhu

2010-01-01

Predicting gene functions is a challenge for biologists in the postgenomic era. Interactions among genes and their products compose networks that can be used to infer gene functions. Most previous studies adopt a linkage assumption, i.e., they assume that gene interactions indicate functional similarities between connected genes. In this study, we propose to use a gene's context graph, i.e., the gene interaction network associated with the focal gene, to infer its functions. In a kernel-based machine-learning framework, we design a context graph kernel to capture the information in context graphs. Our experimental study on a testbed of p53-related genes demonstrates the advantage of using indirect gene interactions and shows the empirical superiority of the proposed approach over linkage-assumption-based methods, such as the algorithm to minimize inconsistent connected genes and diffusion kernels.

Using a fuzzy comprehensive evaluation method to determine product usability: A test case

PubMed Central

Zhou, Ronggang; Chan, Alan H. S.

2016-01-01

BACKGROUND: In order to take into account the inherent uncertainties during product usability evaluation, Zhou and Chan [1] proposed a comprehensive method of usability evaluation for products by combining the analytic hierarchy process (AHP) and fuzzy evaluation methods for synthesizing performance data and subjective response data. This method was designed to provide an integrated framework combining the inevitable vague judgments from the multiple stages of the product evaluation process. OBJECTIVE AND METHODS: In order to illustrate the effectiveness of the model, this study used a summative usability test case to assess the application and strength of the general fuzzy usability framework. To test the proposed fuzzy usability evaluation framework [1], a standard summative usability test was conducted to benchmark the overall usability of a specific network management software. Based on the test data, the fuzzy method was applied to incorporate both the usability scores and uncertainties involved in the multiple components of the evaluation. Then, with Monte Carlo simulation procedures, confidence intervals were used to compare the reliabilities among the fuzzy approach and two typical conventional methods combining metrics based on percentages. RESULTS AND CONCLUSIONS: This case study showed that the fuzzy evaluation technique can be applied successfully for combining summative usability testing data to achieve an overall usability quality for the network software evaluated. Greater differences of confidence interval widths between the method of averaging equally percentage and weighted evaluation method, including the method of weighted percentage averages, verified the strength of the fuzzy method. PMID:28035942

Discovering disease-associated genes in weighted protein-protein interaction networks

NASA Astrophysics Data System (ADS)

Cui, Ying; Cai, Meng; Stanley, H. Eugene

2018-04-01

Although there have been many network-based attempts to discover disease-associated genes, most of them have not taken edge weight - which quantifies their relative strength - into consideration. We use connection weights in a protein-protein interaction (PPI) network to locate disease-related genes. We analyze the topological properties of both weighted and unweighted PPI networks and design an improved random forest classifier to distinguish disease genes from non-disease genes. We use a cross-validation test to confirm that weighted networks are better able to discover disease-associated genes than unweighted networks, which indicates that including link weight in the analysis of network properties provides a better model of complex genotype-phenotype associations.

Learning and tuning fuzzy logic controllers through reinforcements

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.; Khedkar, Pratap

1992-01-01

A new method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system is presented. In particular, our Generalized Approximate Reasoning-based Intelligent Control (GARIC) architecture: (1) learns and tunes a fuzzy logic controller even when only weak reinforcements, such as a binary failure signal, is available; (2) introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; (3) introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and (4) learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward network, which can then adaptively improve performance by using gradient descent methods. We extend the AHC algorithm of Barto, Sutton, and Anderson to include the prior control knowledge of human operators. The GARIC architecture is applied to a cart-pole balancing system and has demonstrated significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing.

Learning and tuning fuzzy logic controllers through reinforcements

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.; Khedkar, Pratap

1992-01-01

This paper presents a new method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system. In particular, our generalized approximate reasoning-based intelligent control (GARIC) architecture (1) learns and tunes a fuzzy logic controller even when only weak reinforcement, such as a binary failure signal, is available; (2) introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; (3) introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and (4) learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward neural network, which can then adaptively improve performance by using gradient descent methods. We extend the AHC algorithm of Barto et al. (1983) to include the prior control knowledge of human operators. The GARIC architecture is applied to a cart-pole balancing system and demonstrates significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing.

Hybrid fuzzy cluster ensemble framework for tumor clustering from biomolecular data.

PubMed

Yu, Zhiwen; Chen, Hantao; You, Jane; Han, Guoqiang; Li, Le

2013-01-01

Cancer class discovery using biomolecular data is one of the most important tasks for cancer diagnosis and treatment. Tumor clustering from gene expression data provides a new way to perform cancer class discovery. Most of the existing research works adopt single-clustering algorithms to perform tumor clustering is from biomolecular data that lack robustness, stability, and accuracy. To further improve the performance of tumor clustering from biomolecular data, we introduce the fuzzy theory into the cluster ensemble framework for tumor clustering from biomolecular data, and propose four kinds of hybrid fuzzy cluster ensemble frameworks (HFCEF), named as HFCEF-I, HFCEF-II, HFCEF-III, and HFCEF-IV, respectively, to identify samples that belong to different types of cancers. The difference between HFCEF-I and HFCEF-II is that they adopt different ensemble generator approaches to generate a set of fuzzy matrices in the ensemble. Specifically, HFCEF-I applies the affinity propagation algorithm (AP) to perform clustering on the sample dimension and generates a set of fuzzy matrices in the ensemble based on the fuzzy membership function and base samples selected by AP. HFCEF-II adopts AP to perform clustering on the attribute dimension, generates a set of subspaces, and obtains a set of fuzzy matrices in the ensemble by performing fuzzy c-means on subspaces. Compared with HFCEF-I and HFCEF-II, HFCEF-III and HFCEF-IV consider the characteristics of HFCEF-I and HFCEF-II. HFCEF-III combines HFCEF-I and HFCEF-II in a serial way, while HFCEF-IV integrates HFCEF-I and HFCEF-II in a concurrent way. HFCEFs adopt suitable consensus functions, such as the fuzzy c-means algorithm or the normalized cut algorithm (Ncut), to summarize generated fuzzy matrices, and obtain the final results. The experiments on real data sets from UCI machine learning repository and cancer gene expression profiles illustrate that 1) the proposed hybrid fuzzy cluster ensemble frameworks work well on real

Structure identification in fuzzy inference using reinforcement learning

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.; Khedkar, Pratap

1993-01-01

In our previous work on the GARIC architecture, we have shown that the system can start with surface structure of the knowledge base (i.e., the linguistic expression of the rules) and learn the deep structure (i.e., the fuzzy membership functions of the labels used in the rules) by using reinforcement learning. Assuming the surface structure, GARIC refines the fuzzy membership functions used in the consequents of the rules using a gradient descent procedure. This hybrid fuzzy logic and reinforcement learning approach can learn to balance a cart-pole system and to backup a truck to its docking location after a few trials. In this paper, we discuss how to do structure identification using reinforcement learning in fuzzy inference systems. This involves identifying both surface as well as deep structure of the knowledge base. The term set of fuzzy linguistic labels used in describing the values of each control variable must be derived. In this process, splitting a label refers to creating new labels which are more granular than the original label and merging two labels creates a more general label. Splitting and merging of labels directly transform the structure of the action selection network used in GARIC by increasing or decreasing the number of hidden layer nodes.

Mining Gene Regulatory Networks by Neural Modeling of Expression Time-Series.

PubMed

Rubiolo, Mariano; Milone, Diego H; Stegmayer, Georgina

2015-01-01

Discovering gene regulatory networks from data is one of the most studied topics in recent years. Neural networks can be successfully used to infer an underlying gene network by modeling expression profiles as times series. This work proposes a novel method based on a pool of neural networks for obtaining a gene regulatory network from a gene expression dataset. They are used for modeling each possible interaction between pairs of genes in the dataset, and a set of mining rules is applied to accurately detect the subjacent relations among genes. The results obtained on artificial and real datasets confirm the method effectiveness for discovering regulatory networks from a proper modeling of the temporal dynamics of gene expression profiles.

The Reconstruction and Analysis of Gene Regulatory Networks.

PubMed

Zheng, Guangyong; Huang, Tao

2018-01-01

In post-genomic era, an important task is to explore the function of individual biological molecules (i.e., gene, noncoding RNA, protein, metabolite) and their organization in living cells. For this end, gene regulatory networks (GRNs) are constructed to show relationship between biological molecules, in which the vertices of network denote biological molecules and the edges of network present connection between nodes (Strogatz, Nature 410:268-276, 2001; Bray, Science 301:1864-1865, 2003). Biologists can understand not only the function of biological molecules but also the organization of components of living cells through interpreting the GRNs, since a gene regulatory network is a comprehensively physiological map of living cells and reflects influence of genetic and epigenetic factors (Strogatz, Nature 410:268-276, 2001; Bray, Science 301:1864-1865, 2003). In this paper, we will review the inference methods of GRN reconstruction and analysis approaches of network structure. As a powerful tool for studying complex diseases and biological processes, the applications of the network method in pathway analysis and disease gene identification will be introduced.

Motorized CPM/CAM physiotherapy device with sliding-mode Fuzzy Neural Network control loop.

PubMed

Ho, Hung-Jung; Chen, Tien-Chi

2009-11-01

Continuous passive motion (CPM) and controllable active motion (CAM) physiotherapy devices promote rehabilitation of damaged joints. This paper presents a computerized CPM/CAM system that obviates the need for mechanical resistance devices such as springs. The system is controlled by a computer which performs sliding-mode Fuzzy Neural Network (FNN) calculations online. CAM-type resistance force is generated by the active performance of an electric motor which is controlled so as to oppose the motion of the patient's leg. A force sensor under the patient's foot on the device pedal provides data for feedback in a sliding-mode FNN control loop built around the motor. Via an active impedance control feedback system, the controller drives the motor to behave similarly to a damped spring by generating and controlling the amplitude and direction of the pedal force in relation to the patient's leg. Experiments demonstrate the high sensitivity and speed of the device. The PC-based feedback nature of the control loop means that sophisticated auto-adaptable CPM/CAM custom-designed physiotherapy becomes possible. The computer base also allows extensive data recording, data analysis and network-connected remote patient monitoring.

Fuzzy logic in control systems: Fuzzy logic controller. I, II

NASA Technical Reports Server (NTRS)

Lee, Chuen Chien

1990-01-01

Recent advances in the theory and applications of fuzzy-logic controllers (FLCs) are examined in an analytical review. The fundamental principles of fuzzy sets and fuzzy logic are recalled; the basic FLC components (fuzzification and defuzzification interfaces, knowledge base, and decision-making logic) are described; and the advantages of FLCs for incorporating expert knowledge into a control system are indicated. Particular attention is given to fuzzy implication functions, the interpretation of sentence connectives (and, also), compositional operators, and inference mechanisms. Applications discussed include the FLC-guided automobile developed by Sugeno and Nishida (1985), FLC hardware systems, FLCs for subway trains and ship-loading cranes, fuzzy-logic chips, and fuzzy computers.

MyGeneFriends: A Social Network Linking Genes, Genetic Diseases, and Researchers.

PubMed

Allot, Alexis; Chennen, Kirsley; Nevers, Yannis; Poidevin, Laetitia; Kress, Arnaud; Ripp, Raymond; Thompson, Julie Dawn; Poch, Olivier; Lecompte, Odile

2017-06-16

The constant and massive increase of biological data offers unprecedented opportunities to decipher the function and evolution of genes and their roles in human diseases. However, the multiplicity of sources and flow of data mean that efficient access to useful information and knowledge production has become a major challenge. This challenge can be addressed by taking inspiration from Web 2.0 and particularly social networks, which are at the forefront of big data exploration and human-data interaction. MyGeneFriends is a Web platform inspired by social networks, devoted to genetic disease analysis, and organized around three types of proactive agents: genes, humans, and genetic diseases. The aim of this study was to improve exploration and exploitation of biological, postgenomic era big data. MyGeneFriends leverages conventions popularized by top social networks (Facebook, LinkedIn, etc), such as networks of friends, profile pages, friendship recommendations, affinity scores, news feeds, content recommendation, and data visualization. MyGeneFriends provides simple and intuitive interactions with data through evaluation and visualization of connections (friendships) between genes, humans, and diseases. The platform suggests new friends and publications and allows agents to follow the activity of their friends. It dynamically personalizes information depending on the user's specific interests and provides an efficient way to share information with collaborators. Furthermore, the user's behavior itself generates new information that constitutes an added value integrated in the network, which can be used to discover new connections between biological agents. We have developed MyGeneFriends, a Web platform leveraging conventions from popular social networks to redefine the relationship between humans and biological big data and improve human processing of biomedical data. MyGeneFriends is available at lbgi.fr/mygenefriends. ©Alexis Allot, Kirsley Chennen, Yannis

A Combination of Extended Fuzzy AHP and Fuzzy GRA for Government E-Tendering in Hybrid Fuzzy Environment

PubMed Central

Wang, Yan; Xi, Chengyu; Zhang, Shuai; Yu, Dejian; Zhang, Wenyu; Li, Yong

2014-01-01

The recent government tendering process being conducted in an electronic way is becoming an inevitable affair for numerous governmental agencies to further exploit the superiorities of conventional tendering. Thus, developing an effective web-based bid evaluation methodology so as to realize an efficient and effective government E-tendering (GeT) system is imperative. This paper firstly investigates the potentiality of employing fuzzy analytic hierarchy process (AHP) along with fuzzy gray relational analysis (GRA) for optimal selection of candidate tenderers in GeT process with consideration of a hybrid fuzzy environment with incomplete weight information. We proposed a novel hybrid fuzzy AHP-GRA (HFAHP-GRA) method that combines an extended fuzzy AHP with a modified fuzzy GRA. The extended fuzzy AHP which combines typical AHP with interval AHP is proposed to obtain the exact weight information, and the modified fuzzy GRA is applied to aggregate different types of evaluation information so as to identify the optimal candidate tenderers. Finally, a prototype system is built and validated with an illustrative example for GeT to confirm the feasibility of our approach. PMID:25057506

A combination of extended fuzzy AHP and fuzzy GRA for government E-tendering in hybrid fuzzy environment.

PubMed

Wang, Yan; Xi, Chengyu; Zhang, Shuai; Yu, Dejian; Zhang, Wenyu; Li, Yong

2014-01-01

The recent government tendering process being conducted in an electronic way is becoming an inevitable affair for numerous governmental agencies to further exploit the superiorities of conventional tendering. Thus, developing an effective web-based bid evaluation methodology so as to realize an efficient and effective government E-tendering (GeT) system is imperative. This paper firstly investigates the potentiality of employing fuzzy analytic hierarchy process (AHP) along with fuzzy gray relational analysis (GRA) for optimal selection of candidate tenderers in GeT process with consideration of a hybrid fuzzy environment with incomplete weight information. We proposed a novel hybrid fuzzy AHP-GRA (HFAHP-GRA) method that combines an extended fuzzy AHP with a modified fuzzy GRA. The extended fuzzy AHP which combines typical AHP with interval AHP is proposed to obtain the exact weight information, and the modified fuzzy GRA is applied to aggregate different types of evaluation information so as to identify the optimal candidate tenderers. Finally, a prototype system is built and validated with an illustrative example for GeT to confirm the feasibility of our approach.

Fuzzy regression modeling for tool performance prediction and degradation detection.

PubMed

Li, X; Er, M J; Lim, B S; Zhou, J H; Gan, O P; Rutkowski, L

2010-10-01

In this paper, the viability of using Fuzzy-Rule-Based Regression Modeling (FRM) algorithm for tool performance and degradation detection is investigated. The FRM is developed based on a multi-layered fuzzy-rule-based hybrid system with Multiple Regression Models (MRM) embedded into a fuzzy logic inference engine that employs Self Organizing Maps (SOM) for clustering. The FRM converts a complex nonlinear problem to a simplified linear format in order to further increase the accuracy in prediction and rate of convergence. The efficacy of the proposed FRM is tested through a case study - namely to predict the remaining useful life of a ball nose milling cutter during a dry machining process of hardened tool steel with a hardness of 52-54 HRc. A comparative study is further made between four predictive models using the same set of experimental data. It is shown that the FRM is superior as compared with conventional MRM, Back Propagation Neural Networks (BPNN) and Radial Basis Function Networks (RBFN) in terms of prediction accuracy and learning speed.

Reactive navigation for autonomous guided vehicle using neuro-fuzzy techniques

NASA Astrophysics Data System (ADS)

Cao, Jin; Liao, Xiaoqun; Hall, Ernest L.

1999-08-01

A Neuro-fuzzy control method for navigation of an Autonomous Guided Vehicle robot is described. Robot navigation is defined as the guiding of a mobile robot to a desired destination or along a desired path in an environment characterized by as terrain and a set of distinct objects, such as obstacles and landmarks. The autonomous navigate ability and road following precision are mainly influenced by its control strategy and real-time control performance. Neural network and fuzzy logic control techniques can improve real-time control performance for mobile robot due to its high robustness and error-tolerance ability. For a mobile robot to navigate automatically and rapidly, an important factor is to identify and classify mobile robots' currently perceptual environment. In this paper, a new approach of the current perceptual environment feature identification and classification, which are based on the analysis of the classifying neural network and the Neuro- fuzzy algorithm, is presented. The significance of this work lies in the development of a new method for mobile robot navigation.

Fuzzy associative memories

NASA Technical Reports Server (NTRS)

Kosko, Bart

1991-01-01

Mappings between fuzzy cubes are discussed. This level of abstraction provides a surprising and fruitful alternative to the propositional and predicate-calculas reasoning techniques used in expert systems. It allows one to reason with sets instead of propositions. Discussed here are fuzzy and neural function estimators, neural vs. fuzzy representation of structured knowledge, fuzzy vector-matrix multiplication, and fuzzy associative memory (FAM) system architecture.

Fuzzy recognition of noncompact musical objects

NASA Astrophysics Data System (ADS)

Cristobal Salas, Alfredo; Tchernykh, Andrei

1997-03-01

This article describes and compares some techniques to extract attributes from black and white images which contain musical objects. The inertia moment, the central moments and the wavelet transform methods are used to describe the images. Two supervised neural networks are applied to classify the images: backpropagation and fuzzy backpropagation. The results are compared.

Stationary and structural control in gene regulatory networks: basic concepts

NASA Astrophysics Data System (ADS)

Dougherty, Edward R.; Pal, Ranadip; Qian, Xiaoning; Bittner, Michael L.; Datta, Aniruddha

2010-01-01

A major reason for constructing gene regulatory networks is to use them as models for determining therapeutic intervention strategies by deriving ways of altering their long-run dynamics in such a way as to reduce the likelihood of entering undesirable states. In general, two paradigms have been taken for gene network intervention: (1) stationary external control is based on optimally altering the status of a control gene (or genes) over time to drive network dynamics; and (2) structural intervention involves an optimal one-time change of the network structure (wiring) to beneficially alter the long-run behaviour of the network. These intervention approaches have mainly been developed within the context of the probabilistic Boolean network model for gene regulation. This article reviews both types of intervention and applies them to reducing the metastatic competence of cells via intervention in a melanoma-related network.

Risk analysis with a fuzzy-logic approach of a complex installation

NASA Astrophysics Data System (ADS)

Peikert, Tim; Garbe, Heyno; Potthast, Stefan

2016-09-01

This paper introduces a procedural method based on fuzzy logic to analyze systematic the risk of an electronic system in an intentional electromagnetic environment (IEME). The method analyzes the susceptibility of a complex electronic installation with respect to intentional electromagnetic interference (IEMI). It combines the advantages of well-known techniques as fault tree analysis (FTA), electromagnetic topology (EMT) and Bayesian networks (BN) and extends the techniques with an approach to handle uncertainty. This approach uses fuzzy sets, membership functions and fuzzy logic to handle the uncertainty with probability functions and linguistic terms. The linguistic terms add to the risk analysis the knowledge from experts of the investigated system or environment.

Gene Network Construction from Microarray Data Identifies a Key Network Module and Several Candidate Hub Genes in Age-Associated Spatial Learning Impairment

PubMed Central

Uddin, Raihan; Singh, Shiva M.

2017-01-01

As humans age many suffer from a decrease in normal brain functions including spatial learning impairments. This study aimed to better understand the molecular mechanisms in age-associated spatial learning impairment (ASLI). We used a mathematical modeling approach implemented in Weighted Gene Co-expression Network Analysis (WGCNA) to create and compare gene network models of young (learning unimpaired) and aged (predominantly learning impaired) brains from a set of exploratory datasets in rats in the context of ASLI. The major goal was to overcome some of the limitations previously observed in the traditional meta- and pathway analysis using these data, and identify novel ASLI related genes and their networks based on co-expression relationship of genes. This analysis identified a set of network modules in the young, each of which is highly enriched with genes functioning in broad but distinct GO functional categories or biological pathways. Interestingly, the analysis pointed to a single module that was highly enriched with genes functioning in “learning and memory” related functions and pathways. Subsequent differential network analysis of this “learning and memory” module in the aged (predominantly learning impaired) rats compared to the young learning unimpaired rats allowed us to identify a set of novel ASLI candidate hub genes. Some of these genes show significant repeatability in networks generated from independent young and aged validation datasets. These hub genes are highly co-expressed with other genes in the network, which not only show differential expression but also differential co-expression and differential connectivity across age and learning impairment. The known function of these hub genes indicate that they play key roles in critical pathways, including kinase and phosphatase signaling, in functions related to various ion channels, and in maintaining neuronal integrity relating to synaptic plasticity and memory formation. Taken

Gene Network Construction from Microarray Data Identifies a Key Network Module and Several Candidate Hub Genes in Age-Associated Spatial Learning Impairment.

PubMed

Uddin, Raihan; Singh, Shiva M

2017-01-01

As humans age many suffer from a decrease in normal brain functions including spatial learning impairments. This study aimed to better understand the molecular mechanisms in age-associated spatial learning impairment (ASLI). We used a mathematical modeling approach implemented in Weighted Gene Co-expression Network Analysis (WGCNA) to create and compare gene network models of young (learning unimpaired) and aged (predominantly learning impaired) brains from a set of exploratory datasets in rats in the context of ASLI. The major goal was to overcome some of the limitations previously observed in the traditional meta- and pathway analysis using these data, and identify novel ASLI related genes and their networks based on co-expression relationship of genes. This analysis identified a set of network modules in the young, each of which is highly enriched with genes functioning in broad but distinct GO functional categories or biological pathways. Interestingly, the analysis pointed to a single module that was highly enriched with genes functioning in "learning and memory" related functions and pathways. Subsequent differential network analysis of this "learning and memory" module in the aged (predominantly learning impaired) rats compared to the young learning unimpaired rats allowed us to identify a set of novel ASLI candidate hub genes. Some of these genes show significant repeatability in networks generated from independent young and aged validation datasets. These hub genes are highly co-expressed with other genes in the network, which not only show differential expression but also differential co-expression and differential connectivity across age and learning impairment. The known function of these hub genes indicate that they play key roles in critical pathways, including kinase and phosphatase signaling, in functions related to various ion channels, and in maintaining neuronal integrity relating to synaptic plasticity and memory formation. Taken together, they

Modeling gene regulatory networks: A network simplification algorithm

NASA Astrophysics Data System (ADS)

Ferreira, Luiz Henrique O.; de Castro, Maria Clicia S.; da Silva, Fabricio A. B.

2016-12-01

Boolean networks have been used for some time to model Gene Regulatory Networks (GRNs), which describe cell functions. Those models can help biologists to make predictions, prognosis and even specialized treatment when some disturb on the GRN lead to a sick condition. However, the amount of information related to a GRN can be huge, making the task of inferring its boolean network representation quite a challenge. The method shown here takes into account information about the interactome to build a network, where each node represents a protein, and uses the entropy of each node as a key to reduce the size of the network, allowing the further inferring process to focus only on the main protein hubs, the ones with most potential to interfere in overall network behavior.

Topology association analysis in weighted protein interaction network for gene prioritization

NASA Astrophysics Data System (ADS)

Wu, Shunyao; Shao, Fengjing; Zhang, Qi; Ji, Jun; Xu, Shaojie; Sun, Rencheng; Sun, Gengxin; Du, Xiangjun; Sui, Yi

2016-11-01

Although lots of algorithms for disease gene prediction have been proposed, the weights of edges are rarely taken into account. In this paper, the strengths of topology associations between disease and essential genes are analyzed in weighted protein interaction network. Empirical analysis demonstrates that compared to other genes, disease genes are weakly connected with essential genes in protein interaction network. Based on this finding, a novel global distance measurement for gene prioritization with weighted protein interaction network is proposed in this paper. Positive and negative flow is allocated to disease and essential genes, respectively. Additionally network propagation model is extended for weighted network. Experimental results on 110 diseases verify the effectiveness and potential of the proposed measurement. Moreover, weak links play more important role than strong links for gene prioritization, which is meaningful to deeply understand protein interaction network.

Construct and Compare Gene Coexpression Networks with DAPfinder and DAPview.

PubMed

Skinner, Jeff; Kotliarov, Yuri; Varma, Sudhir; Mine, Karina L; Yambartsev, Anatoly; Simon, Richard; Huyen, Yentram; Morgun, Andrey

2011-07-14

DAPfinder and DAPview are novel BRB-ArrayTools plug-ins to construct gene coexpression networks and identify significant differences in pairwise gene-gene coexpression between two phenotypes. Each significant difference in gene-gene association represents a Differentially Associated Pair (DAP). Our tools include several choices of filtering methods, gene-gene association metrics, statistical testing methods and multiple comparison adjustments. Network results are easily displayed in Cytoscape. Analyses of glioma experiments and microarray simulations demonstrate the utility of these tools. DAPfinder is a new friendly-user tool for reconstruction and comparison of biological networks.

Impulsive effect on global exponential stability of BAM fuzzy cellular neural networks with time-varying delays

NASA Astrophysics Data System (ADS)

Li, Kelin

2010-02-01

In this article, a class of impulsive bidirectional associative memory (BAM) fuzzy cellular neural networks (FCNNs) with time-varying delays is formulated and investigated. By employing delay differential inequality and M-matrix theory, some sufficient conditions ensuring the existence, uniqueness and global exponential stability of equilibrium point for impulsive BAM FCNNs with time-varying delays are obtained. In particular, a precise estimate of the exponential convergence rate is also provided, which depends on system parameters and impulsive perturbation intention. It is believed that these results are significant and useful for the design and applications of BAM FCNNs. An example is given to show the effectiveness of the results obtained here.

Combinational Reasoning of Quantitative Fuzzy Topological Relations for Simple Fuzzy Regions

PubMed Central

Liu, Bo; Li, Dajun; Xia, Yuanping; Ruan, Jian; Xu, Lili; Wu, Huanyi

2015-01-01

In recent years, formalization and reasoning of topological relations have become a hot topic as a means to generate knowledge about the relations between spatial objects at the conceptual and geometrical levels. These mechanisms have been widely used in spatial data query, spatial data mining, evaluation of equivalence and similarity in a spatial scene, as well as for consistency assessment of the topological relations of multi-resolution spatial databases. The concept of computational fuzzy topological space is applied to simple fuzzy regions to efficiently and more accurately solve fuzzy topological relations. Thus, extending the existing research and improving upon the previous work, this paper presents a new method to describe fuzzy topological relations between simple spatial regions in Geographic Information Sciences (GIS) and Artificial Intelligence (AI). Firstly, we propose a new definition for simple fuzzy line segments and simple fuzzy regions based on the computational fuzzy topology. And then, based on the new definitions, we also propose a new combinational reasoning method to compute the topological relations between simple fuzzy regions, moreover, this study has discovered that there are (1) 23 different topological relations between a simple crisp region and a simple fuzzy region; (2) 152 different topological relations between two simple fuzzy regions. In the end, we have discussed some examples to demonstrate the validity of the new method, through comparisons with existing fuzzy models, we showed that the proposed method can compute more than the existing models, as it is more expressive than the existing fuzzy models. PMID:25775452

Emergent fuzzy geometry and fuzzy physics in four dimensions

NASA Astrophysics Data System (ADS)

Ydri, Badis; Rouag, Ahlam; Ramda, Khaled

2017-03-01

A detailed Monte Carlo calculation of the phase diagram of bosonic mass-deformed IKKT Yang-Mills matrix models in three and six dimensions with quartic mass deformations is given. Background emergent fuzzy geometries in two and four dimensions are observed with a fluctuation given by a noncommutative U (1) gauge theory very weakly coupled to normal scalar fields. The geometry, which is determined dynamically, is given by the fuzzy spheres SN2 and SN2 × SN2 respectively. The three and six matrix models are effectively in the same universality class. For example, in two dimensions the geometry is completely stable, whereas in four dimensions the geometry is stable only in the limit M ⟶ ∞, where M is the mass of the normal fluctuations. The behaviors of the eigenvalue distribution in the two theories are also different. We also sketch how we can obtain a stable fuzzy four-sphere SN2 × SN2 in the large N limit for all values of M as well as models of topology change in which the transition between spheres of different dimensions is observed. The stable fuzzy spheres in two and four dimensions act precisely as regulators which is the original goal of fuzzy geometry and fuzzy physics. Fuzzy physics and fuzzy field theory on these spaces are briefly discussed.

Neuro-Fuzzy Computational Technique to Control Load Frequency in Hydro-Thermal Interconnected Power System

NASA Astrophysics Data System (ADS)

Prakash, S.; Sinha, S. K.

2015-09-01

In this research work, two areas hydro-thermal power system connected through tie-lines is considered. The perturbation of frequencies at the areas and resulting tie line power flows arise due to unpredictable load variations that cause mismatch between the generated and demanded powers. Due to rising and falling power demand, the real and reactive power balance is harmed; hence frequency and voltage get deviated from nominal value. This necessitates designing of an accurate and fast controller to maintain the system parameters at nominal value. The main purpose of system generation control is to balance the system generation against the load and losses so that the desired frequency and power interchange between neighboring systems are maintained. The intelligent controllers like fuzzy logic, artificial neural network (ANN) and hybrid fuzzy neural network approaches are used for automatic generation control for the two area interconnected power systems. Area 1 consists of thermal reheat power plant whereas area 2 consists of hydro power plant with electric governor. Performance evaluation is carried out by using intelligent (ANFIS, ANN and fuzzy) control and conventional PI and PID control approaches. To enhance the performance of controller sliding surface i.e. variable structure control is included. The model of interconnected power system has been developed with all five types of said controllers and simulated using MATLAB/SIMULINK package. The performance of the intelligent controllers has been compared with the conventional PI and PID controllers for the interconnected power system. A comparison of ANFIS, ANN, Fuzzy and PI, PID based approaches shows the superiority of proposed ANFIS over ANN, fuzzy and PI, PID. Thus the hybrid fuzzy neural network controller has better dynamic response i.e., quick in operation, reduced error magnitude and minimized frequency transients.

Inferring gene dependency network specific to phenotypic alteration based on gene expression data and clinical information of breast cancer.

PubMed

Zhou, Xionghui; Liu, Juan

2014-01-01

Although many methods have been proposed to reconstruct gene regulatory network, most of them, when applied in the sample-based data, can not reveal the gene regulatory relations underlying the phenotypic change (e.g. normal versus cancer). In this paper, we adopt phenotype as a variable when constructing the gene regulatory network, while former researches either neglected it or only used it to select the differentially expressed genes as the inputs to construct the gene regulatory network. To be specific, we integrate phenotype information with gene expression data to identify the gene dependency pairs by using the method of conditional mutual information. A gene dependency pair (A,B) means that the influence of gene A on the phenotype depends on gene B. All identified gene dependency pairs constitute a directed network underlying the phenotype, namely gene dependency network. By this way, we have constructed gene dependency network of breast cancer from gene expression data along with two different phenotype states (metastasis and non-metastasis). Moreover, we have found the network scale free, indicating that its hub genes with high out-degrees may play critical roles in the network. After functional investigation, these hub genes are found to be biologically significant and specially related to breast cancer, which suggests that our gene dependency network is meaningful. The validity has also been justified by literature investigation. From the network, we have selected 43 discriminative hubs as signature to build the classification model for distinguishing the distant metastasis risks of breast cancer patients, and the result outperforms those classification models with published signatures. In conclusion, we have proposed a promising way to construct the gene regulatory network by using sample-based data, which has been shown to be effective and accurate in uncovering the hidden mechanism of the biological process and identifying the gene signature for

Quantification of sand fraction from seismic attributes using Neuro-Fuzzy approach

NASA Astrophysics Data System (ADS)

Verma, Akhilesh K.; Chaki, Soumi; Routray, Aurobinda; Mohanty, William K.; Jenamani, Mamata

2014-12-01

In this paper, we illustrate the modeling of a reservoir property (sand fraction) from seismic attributes namely seismic impedance, seismic amplitude, and instantaneous frequency using Neuro-Fuzzy (NF) approach. Input dataset includes 3D post-stacked seismic attributes and six well logs acquired from a hydrocarbon field located in the western coast of India. Presence of thin sand and shale layers in the basin area makes the modeling of reservoir characteristic a challenging task. Though seismic data is helpful in extrapolation of reservoir properties away from boreholes; yet, it could be challenging to delineate thin sand and shale reservoirs using seismic data due to its limited resolvability. Therefore, it is important to develop state-of-art intelligent methods for calibrating a nonlinear mapping between seismic data and target reservoir variables. Neural networks have shown its potential to model such nonlinear mappings; however, uncertainties associated with the model and datasets are still a concern. Hence, introduction of Fuzzy Logic (FL) is beneficial for handling these uncertainties. More specifically, hybrid variants of Artificial Neural Network (ANN) and fuzzy logic, i.e., NF methods, are capable for the modeling reservoir characteristics by integrating the explicit knowledge representation power of FL with the learning ability of neural networks. In this paper, we opt for ANN and three different categories of Adaptive Neuro-Fuzzy Inference System (ANFIS) based on clustering of the available datasets. A comparative analysis of these three different NF models (i.e., Sugeno-type fuzzy inference systems using a grid partition on the data (Model 1), using subtractive clustering (Model 2), and using Fuzzy c-means (FCM) clustering (Model 3)) and ANN suggests that Model 3 has outperformed its counterparts in terms of performance evaluators on the present dataset. Performance of the selected algorithms is evaluated in terms of correlation coefficients (CC), root

Relationships between probabilistic Boolean networks and dynamic Bayesian networks as models of gene regulatory networks

PubMed Central

Lähdesmäki, Harri; Hautaniemi, Sampsa; Shmulevich, Ilya; Yli-Harja, Olli

2006-01-01

A significant amount of attention has recently been focused on modeling of gene regulatory networks. Two frequently used large-scale modeling frameworks are Bayesian networks (BNs) and Boolean networks, the latter one being a special case of its recent stochastic extension, probabilistic Boolean networks (PBNs). PBN is a promising model class that generalizes the standard rule-based interactions of Boolean networks into the stochastic setting. Dynamic Bayesian networks (DBNs) is a general and versatile model class that is able to represent complex temporal stochastic processes and has also been proposed as a model for gene regulatory systems. In this paper, we concentrate on these two model classes and demonstrate that PBNs and a certain subclass of DBNs can represent the same joint probability distribution over their common variables. The major benefit of introducing the relationships between the models is that it opens up the possibility of applying the standard tools of DBNs to PBNs and vice versa. Hence, the standard learning tools of DBNs can be applied in the context of PBNs, and the inference methods give a natural way of handling the missing values in PBNs which are often present in gene expression measurements. Conversely, the tools for controlling the stationary behavior of the networks, tools for projecting networks onto sub-networks, and efficient learning schemes can be used for DBNs. In other words, the introduced relationships between the models extend the collection of analysis tools for both model classes. PMID:17415411

Reveal genes functionally associated with ACADS by a network study.

PubMed

Chen, Yulong; Su, Zhiguang

2015-09-15

Establishing a systematic network is aimed at finding essential human gene-gene/gene-disease pathway by means of network inter-connecting patterns and functional annotation analysis. In the present study, we have analyzed functional gene interactions of short-chain acyl-coenzyme A dehydrogenase gene (ACADS). ACADS plays a vital role in free fatty acid β-oxidation and regulates energy homeostasis. Modules of highly inter-connected genes in disease-specific ACADS network are derived by integrating gene function and protein interaction data. Among the 8 genes in ACADS web retrieved from both STRING and GeneMANIA, ACADS is effectively conjoined with 4 genes including HAHDA, HADHB, ECHS1 and ACAT1. The functional analysis is done via ontological briefing and candidate disease identification. We observed that the highly efficient-interlinked genes connected with ACADS are HAHDA, HADHB, ECHS1 and ACAT1. Interestingly, the ontological aspect of genes in the ACADS network reveals that ACADS, HAHDA and HADHB play equally vital roles in fatty acid metabolism. The gene ACAT1 together with ACADS indulges in ketone metabolism. Our computational gene web analysis also predicts potential candidate disease recognition, thus indicating the involvement of ACADS, HAHDA, HADHB, ECHS1 and ACAT1 not only with lipid metabolism but also with infant death syndrome, skeletal myopathy, acute hepatic encephalopathy, Reye-like syndrome, episodic ketosis, and metabolic acidosis. The current study presents a comprehensible layout of ACADS network, its functional strategies and candidate disease approach associated with ACADS network. Copyright © 2015 Elsevier B.V. All rights reserved.

Finite-time stability and synchronization of memristor-based fractional-order fuzzy cellular neural networks

NASA Astrophysics Data System (ADS)

Zheng, Mingwen; Li, Lixiang; Peng, Haipeng; Xiao, Jinghua; Yang, Yixian; Zhang, Yanping; Zhao, Hui

2018-06-01

This paper mainly studies the finite-time stability and synchronization problems of memristor-based fractional-order fuzzy cellular neural network (MFFCNN). Firstly, we discuss the existence and uniqueness of the Filippov solution of the MFFCNN according to the Banach fixed point theorem and give a sufficient condition for the existence and uniqueness of the solution. Secondly, a sufficient condition to ensure the finite-time stability of the MFFCNN is obtained based on the definition of finite-time stability of the MFFCNN and Gronwall-Bellman inequality. Thirdly, by designing a simple linear feedback controller, the finite-time synchronization criterion for drive-response MFFCNN systems is derived according to the definition of finite-time synchronization. These sufficient conditions are easy to verify. Finally, two examples are given to show the effectiveness of the proposed results.

Systematic Evaluation of Molecular Networks for Discovery of Disease Genes.

PubMed

Huang, Justin K; Carlin, Daniel E; Yu, Michael Ku; Zhang, Wei; Kreisberg, Jason F; Tamayo, Pablo; Ideker, Trey

2018-04-25

Gene networks are rapidly growing in size and number, raising the question of which networks are most appropriate for particular applications. Here, we evaluate 21 human genome-wide interaction networks for their ability to recover 446 disease gene sets identified through literature curation, gene expression profiling, or genome-wide association studies. While all networks have some ability to recover disease genes, we observe a wide range of performance with STRING, ConsensusPathDB, and GIANT networks having the best performance overall. A general tendency is that performance scales with network size, suggesting that new interaction discovery currently outweighs the detrimental effects of false positives. Correcting for size, we find that the DIP network provides the highest efficiency (value per interaction). Based on these results, we create a parsimonious composite network with both high efficiency and performance. This work provides a benchmark for selection of molecular networks in human disease research. Copyright © 2018 Elsevier Inc. All rights reserved.

Application of Fault Tree Analysis and Fuzzy Neural Networks to Fault Diagnosis in the Internet of Things (IoT) for Aquaculture.

PubMed

Chen, Yingyi; Zhen, Zhumi; Yu, Huihui; Xu, Jing

2017-01-14

In the Internet of Things (IoT) equipment used for aquaculture is often deployed in outdoor ponds located in remote areas. Faults occur frequently in these tough environments and the staff generally lack professional knowledge and pay a low degree of attention in these areas. Once faults happen, expert personnel must carry out maintenance outdoors. Therefore, this study presents an intelligent method for fault diagnosis based on fault tree analysis and a fuzzy neural network. In the proposed method, first, the fault tree presents a logic structure of fault symptoms and faults. Second, rules extracted from the fault trees avoid duplicate and redundancy. Third, the fuzzy neural network is applied to train the relationship mapping between fault symptoms and faults. In the aquaculture IoT, one fault can cause various fault symptoms, and one symptom can be caused by a variety of faults. Four fault relationships are obtained. Results show that one symptom-to-one fault, two symptoms-to-two faults, and two symptoms-to-one fault relationships can be rapidly diagnosed with high precision, while one symptom-to-two faults patterns perform not so well, but are still worth researching. This model implements diagnosis for most kinds of faults in the aquaculture IoT.

Application of Fault Tree Analysis and Fuzzy Neural Networks to Fault Diagnosis in the Internet of Things (IoT) for Aquaculture

PubMed Central

Chen, Yingyi; Zhen, Zhumi; Yu, Huihui; Xu, Jing

2017-01-01

In the Internet of Things (IoT) equipment used for aquaculture is often deployed in outdoor ponds located in remote areas. Faults occur frequently in these tough environments and the staff generally lack professional knowledge and pay a low degree of attention in these areas. Once faults happen, expert personnel must carry out maintenance outdoors. Therefore, this study presents an intelligent method for fault diagnosis based on fault tree analysis and a fuzzy neural network. In the proposed method, first, the fault tree presents a logic structure of fault symptoms and faults. Second, rules extracted from the fault trees avoid duplicate and redundancy. Third, the fuzzy neural network is applied to train the relationship mapping between fault symptoms and faults. In the aquaculture IoT, one fault can cause various fault symptoms, and one symptom can be caused by a variety of faults. Four fault relationships are obtained. Results show that one symptom-to-one fault, two symptoms-to-two faults, and two symptoms-to-one fault relationships can be rapidly diagnosed with high precision, while one symptom-to-two faults patterns perform not so well, but are still worth researching. This model implements diagnosis for most kinds of faults in the aquaculture IoT. PMID:28098822

Reverse engineering and analysis of large genome-scale gene networks

PubMed Central

Aluru, Maneesha; Zola, Jaroslaw; Nettleton, Dan; Aluru, Srinivas

2013-01-01

Reverse engineering the whole-genome networks of complex multicellular organisms continues to remain a challenge. While simpler models easily scale to large number of genes and gene expression datasets, more accurate models are compute intensive limiting their scale of applicability. To enable fast and accurate reconstruction of large networks, we developed Tool for Inferring Network of Genes (TINGe), a parallel mutual information (MI)-based program. The novel features of our approach include: (i) B-spline-based formulation for linear-time computation of MI, (ii) a novel algorithm for direct permutation testing and (iii) development of parallel algorithms to reduce run-time and facilitate construction of large networks. We assess the quality of our method by comparison with ARACNe (Algorithm for the Reconstruction of Accurate Cellular Networks) and GeneNet and demonstrate its unique capability by reverse engineering the whole-genome network of Arabidopsis thaliana from 3137 Affymetrix ATH1 GeneChips in just 9 min on a 1024-core cluster. We further report on the development of a new software Gene Network Analyzer (GeNA) for extracting context-specific subnetworks from a given set of seed genes. Using TINGe and GeNA, we performed analysis of 241 Arabidopsis AraCyc 8.0 pathways, and the results are made available through the web. PMID:23042249

Gene expression patterns combined with network analysis identify hub genes associated with bladder cancer.

PubMed

Bi, Dongbin; Ning, Hao; Liu, Shuai; Que, Xinxiang; Ding, Kejia

2015-06-01

To explore molecular mechanisms of bladder cancer (BC), network strategy was used to find biomarkers for early detection and diagnosis. The differentially expressed genes (DEGs) between bladder carcinoma patients and normal subjects were screened using empirical Bayes method of the linear models for microarray data package. Co-expression networks were constructed by differentially co-expressed genes and links. Regulatory impact factors (RIF) metric was used to identify critical transcription factors (TFs). The protein-protein interaction (PPI) networks were constructed by the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and clusters were obtained through molecular complex detection (MCODE) algorithm. Centralities analyses for complex networks were performed based on degree, stress and betweenness. Enrichment analyses were performed based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Co-expression networks and TFs (based on expression data of global DEGs and DEGs in different stages and grades) were identified. Hub genes of complex networks, such as UBE2C, ACTA2, FABP4, CKS2, FN1 and TOP2A, were also obtained according to analysis of degree. In gene enrichment analyses of global DEGs, cell adhesion, proteinaceous extracellular matrix and extracellular matrix structural constituent were top three GO terms. ECM-receptor interaction, focal adhesion, and cell cycle were significant pathways. Our results provide some potential underlying biomarkers of BC. However, further validation is required and deep studies are needed to elucidate the pathogenesis of BC. Copyright © 2015 Elsevier Ltd. All rights reserved.

Spatial interpolation and radiological mapping of ambient gamma dose rate by using artificial neural networks and fuzzy logic methods.

PubMed

Yeşilkanat, Cafer Mert; Kobya, Yaşar; Taşkın, Halim; Çevik, Uğur

2017-09-01

The aim of this study was to determine spatial risk dispersion of ambient gamma dose rate (AGDR) by using both artificial neural network (ANN) and fuzzy logic (FL) methods, compare the performances of methods, make dose estimations for intermediate stations with no previous measurements and create dose rate risk maps of the study area. In order to determine the dose distribution by using artificial neural networks, two main networks and five different network structures were used; feed forward ANN; Multi-layer perceptron (MLP), Radial basis functional neural network (RBFNN), Quantile regression neural network (QRNN) and recurrent ANN; Jordan networks (JN), Elman networks (EN). In the evaluation of estimation performance obtained for the test data, all models appear to give similar results. According to the cross-validation results obtained for explaining AGDR distribution, Pearson's r coefficients were calculated as 0.94, 0.91, 0.89, 0.91, 0.91 and 0.92 and RMSE values were calculated as 34.78, 43.28, 63.92, 44.86, 46.77 and 37.92 for MLP, RBFNN, QRNN, JN, EN and FL, respectively. In addition, spatial risk maps showing distributions of AGDR of the study area were created by all models and results were compared with geological, topological and soil structure. Copyright © 2017 Elsevier Ltd. All rights reserved.

Gene regulation is governed by a core network in hepatocellular carcinoma.

PubMed

Gu, Zuguang; Zhang, Chenyu; Wang, Jin

2012-05-01

Hepatocellular carcinoma (HCC) is one of the most lethal cancers worldwide, and the mechanisms that lead to the disease are still relatively unclear. However, with the development of high-throughput technologies it is possible to gain a systematic view of biological systems to enhance the understanding of the roles of genes associated with HCC. Thus, analysis of the mechanism of molecule interactions in the context of gene regulatory networks can reveal specific sub-networks that lead to the development of HCC. In this study, we aimed to identify the most important gene regulations that are dysfunctional in HCC generation. Our method for constructing gene regulatory network is based on predicted target interactions, experimentally-supported interactions, and co-expression model. Regulators in the network included both transcription factors and microRNAs to provide a complete view of gene regulation. Analysis of gene regulatory network revealed that gene regulation in HCC is highly modular, in which different sets of regulators take charge of specific biological processes. We found that microRNAs mainly control biological functions related to mitochondria and oxidative reduction, while transcription factors control immune responses, extracellular activity and the cell cycle. On the higher level of gene regulation, there exists a core network that organizes regulations between different modules and maintains the robustness of the whole network. There is direct experimental evidence for most of the regulators in the core gene regulatory network relating to HCC. We infer it is the central controller of gene regulation. Finally, we explored the influence of the core gene regulatory network on biological pathways. Our analysis provides insights into the mechanism of transcriptional and post-transcriptional control in HCC. In particular, we highlight the importance of the core gene regulatory network; we propose that it is highly related to HCC and we believe further

Fuzzylot: a novel self-organising fuzzy-neural rule-based pilot system for automated vehicles.

PubMed

Pasquier, M; Quek, C; Toh, M

2001-10-01

This paper presents part of our research work concerned with the realisation of an Intelligent Vehicle and the technologies required for its routing, navigation, and control. An automated driver prototype has been developed using a self-organising fuzzy rule-based system (POPFNN-CRI(S)) to model and subsequently emulate human driving expertise. The ability of fuzzy logic to represent vague information using linguistic variables makes it a powerful tool to develop rule-based control systems when an exact working model is not available, as is the case of any vehicle-driving task. Designing a fuzzy system, however, is a complex endeavour, due to the need to define the variables and their associated fuzzy sets, and determine a suitable rule base. Many efforts have thus been devoted to automating this process, yielding the development of learning and optimisation techniques. One of them is the family of POP-FNNs, or Pseudo-Outer Product Fuzzy Neural Networks (TVR, AARS(S), AARS(NS), CRI, Yager). These generic self-organising neural networks developed at the Intelligent Systems Laboratory (ISL/NTU) are based on formal fuzzy mathematical theory and are able to objectively extract a fuzzy rule base from training data. In this application, a driving simulator has been developed, that integrates a detailed model of the car dynamics, complete with engine characteristics and environmental parameters, and an OpenGL-based 3D-simulation interface coupled with driving wheel and accelerator/ brake pedals. The simulator has been used on various road scenarios to record from a human pilot driving data consisting of steering and speed control actions associated to road features. Specifically, the POPFNN-CRI(S) system is used to cluster the data and extract a fuzzy rule base modelling the human driving behaviour. Finally, the effectiveness of the generated rule base has been validated using the simulator in autopilot mode.

Fuzzy Subspace Clustering

NASA Astrophysics Data System (ADS)

Borgelt, Christian

In clustering we often face the situation that only a subset of the available attributes is relevant for forming clusters, even though this may not be known beforehand. In such cases it is desirable to have a clustering algorithm that automatically weights attributes or even selects a proper subset. In this paper I study such an approach for fuzzy clustering, which is based on the idea to transfer an alternative to the fuzzifier (Klawonn and Höppner, What is fuzzy about fuzzy clustering? Understanding and improving the concept of the fuzzifier, In: Proc. 5th Int. Symp. on Intelligent Data Analysis, 254-264, Springer, Berlin, 2003) to attribute weighting fuzzy clustering (Keller and Klawonn, Int J Uncertain Fuzziness Knowl Based Syst 8:735-746, 2000). In addition, by reformulating Gustafson-Kessel fuzzy clustering, a scheme for weighting and selecting principal axes can be obtained. While in Borgelt (Feature weighting and feature selection in fuzzy clustering, In: Proc. 17th IEEE Int. Conf. on Fuzzy Systems, IEEE Press, Piscataway, NJ, 2008) I already presented such an approach for a global selection of attributes and principal axes, this paper extends it to a cluster-specific selection, thus arriving at a fuzzy subspace clustering algorithm (Parsons, Haque, and Liu, 2004).

Prioritizing chronic obstructive pulmonary disease (COPD) candidate genes in COPD-related networks

PubMed Central

Zhang, Yihua; Li, Wan; Feng, Yuyan; Guo, Shanshan; Zhao, Xilei; Wang, Yahui; He, Yuehan; He, Weiming; Chen, Lina

2017-01-01

Chronic obstructive pulmonary disease (COPD) is a multi-factor disease, which could be caused by many factors, including disturbances of metabolism and protein-protein interactions (PPIs). In this paper, a weighted COPD-related metabolic network and a weighted COPD-related PPI network were constructed base on COPD disease genes and functional information. Candidate genes in these weighted COPD-related networks were prioritized by making use of a gene prioritization method, respectively. Literature review and functional enrichment analysis of the top 100 genes in these two networks suggested the correlation of COPD and these genes. The performance of our gene prioritization method was superior to that of ToppGene and ToppNet for genes from the COPD-related metabolic network or the COPD-related PPI network after assessing using leave-one-out cross-validation, literature validation and functional enrichment analysis. The top-ranked genes prioritized from COPD-related metabolic and PPI networks could promote the better understanding about the molecular mechanism of this disease from different perspectives. The top 100 genes in COPD-related metabolic network or COPD-related PPI network might be potential markers for the diagnosis and treatment of COPD. PMID:29262568

Prioritizing chronic obstructive pulmonary disease (COPD) candidate genes in COPD-related networks.

PubMed

Zhang, Yihua; Li, Wan; Feng, Yuyan; Guo, Shanshan; Zhao, Xilei; Wang, Yahui; He, Yuehan; He, Weiming; Chen, Lina

2017-11-28

Chronic obstructive pulmonary disease (COPD) is a multi-factor disease, which could be caused by many factors, including disturbances of metabolism and protein-protein interactions (PPIs). In this paper, a weighted COPD-related metabolic network and a weighted COPD-related PPI network were constructed base on COPD disease genes and functional information. Candidate genes in these weighted COPD-related networks were prioritized by making use of a gene prioritization method, respectively. Literature review and functional enrichment analysis of the top 100 genes in these two networks suggested the correlation of COPD and these genes. The performance of our gene prioritization method was superior to that of ToppGene and ToppNet for genes from the COPD-related metabolic network or the COPD-related PPI network after assessing using leave-one-out cross-validation, literature validation and functional enrichment analysis. The top-ranked genes prioritized from COPD-related metabolic and PPI networks could promote the better understanding about the molecular mechanism of this disease from different perspectives. The top 100 genes in COPD-related metabolic network or COPD-related PPI network might be potential markers for the diagnosis and treatment of COPD.

Fuzzy multi-objective chance-constrained programming model for hazardous materials transportation

NASA Astrophysics Data System (ADS)

Du, Jiaoman; Yu, Lean; Li, Xiang

2016-04-01

Hazardous materials transportation is an important and hot issue of public safety. Based on the shortest path model, this paper presents a fuzzy multi-objective programming model that minimizes the transportation risk to life, travel time and fuel consumption. First, we present the risk model, travel time model and fuel consumption model. Furthermore, we formulate a chance-constrained programming model within the framework of credibility theory, in which the lengths of arcs in the transportation network are assumed to be fuzzy variables. A hybrid intelligent algorithm integrating fuzzy simulation and genetic algorithm is designed for finding a satisfactory solution. Finally, some numerical examples are given to demonstrate the efficiency of the proposed model and algorithm.

Adaptive fuzzy leader clustering of complex data sets in pattern recognition

NASA Technical Reports Server (NTRS)

Newton, Scott C.; Pemmaraju, Surya; Mitra, Sunanda

1992-01-01

A modular, unsupervised neural network architecture for clustering and classification of complex data sets is presented. The adaptive fuzzy leader clustering (AFLC) architecture is a hybrid neural-fuzzy system that learns on-line in a stable and efficient manner. The initial classification is performed in two stages: a simple competitive stage and a distance metric comparison stage. The cluster prototypes are then incrementally updated by relocating the centroid positions from fuzzy C-means system equations for the centroids and the membership values. The AFLC algorithm is applied to the Anderson Iris data and laser-luminescent fingerprint image data. It is concluded that the AFLC algorithm successfully classifies features extracted from real data, discrete or continuous.

A Fuzzy-Based Decision Support Model for Selecting the Best Dialyser Flux in Haemodialysis.

PubMed

Oztürk, Necla; Tozan, Hakan

2015-01-01

Decision making is an important procedure for every organization. The procedure is particularly challenging for complicated multi-criteria problems. Selection of dialyser flux is one of the decisions routinely made for haemodialysis treatment provided for chronic kidney failure patients. This study provides a decision support model for selecting the best dialyser flux between high-flux and low-flux dialyser alternatives. The preferences of decision makers were collected via a questionnaire. A total of 45 questionnaires filled by dialysis physicians and nephrologists were assessed. A hybrid fuzzy-based decision support software that enables the use of Analytic Hierarchy Process (AHP), Fuzzy Analytic Hierarchy Process (FAHP), Analytic Network Process (ANP), and Fuzzy Analytic Network Process (FANP) was used to evaluate the flux selection model. In conclusion, the results showed that a high-flux dialyser is the best. option for haemodialysis treatment.

A statistical method for measuring activation of gene regulatory networks.

PubMed

Esteves, Gustavo H; Reis, Luiz F L

2018-06-13

Gene expression data analysis is of great importance for modern molecular biology, given our ability to measure the expression profiles of thousands of genes and enabling studies rooted in systems biology. In this work, we propose a simple statistical model for the activation measuring of gene regulatory networks, instead of the traditional gene co-expression networks. We present the mathematical construction of a statistical procedure for testing hypothesis regarding gene regulatory network activation. The real probability distribution for the test statistic is evaluated by a permutation based study. To illustrate the functionality of the proposed methodology, we also present a simple example based on a small hypothetical network and the activation measuring of two KEGG networks, both based on gene expression data collected from gastric and esophageal samples. The two KEGG networks were also analyzed for a public database, available through NCBI-GEO, presented as Supplementary Material. This method was implemented in an R package that is available at the BioConductor project website under the name maigesPack.

An extensive analysis of disease-gene associations using network integration and fast kernel-based gene prioritization methods.

PubMed

Valentini, Giorgio; Paccanaro, Alberto; Caniza, Horacio; Romero, Alfonso E; Re, Matteo

2014-06-01

In the context of "network medicine", gene prioritization methods represent one of the main tools to discover candidate disease genes by exploiting the large amount of data covering different types of functional relationships between genes. Several works proposed to integrate multiple sources of data to improve disease gene prioritization, but to our knowledge no systematic studies focused on the quantitative evaluation of the impact of network integration on gene prioritization. In this paper, we aim at providing an extensive analysis of gene-disease associations not limited to genetic disorders, and a systematic comparison of different network integration methods for gene prioritization. We collected nine different functional networks representing different functional relationships between genes, and we combined them through both unweighted and weighted network integration methods. We then prioritized genes with respect to each of the considered 708 medical subject headings (MeSH) diseases by applying classical guilt-by-association, random walk and random walk with restart algorithms, and the recently proposed kernelized score functions. The results obtained with classical random walk algorithms and the best single network achieved an average area under the curve (AUC) across the 708 MeSH diseases of about 0.82, while kernelized score functions and network integration boosted the average AUC to about 0.89. Weighted integration, by exploiting the different "informativeness" embedded in different functional networks, outperforms unweighted integration at 0.01 significance level, according to the Wilcoxon signed rank sum test. For each MeSH disease we provide the top-ranked unannotated candidate genes, available for further bio-medical investigation. Network integration is necessary to boost the performances of gene prioritization methods. Moreover the methods based on kernelized score functions can further enhance disease gene ranking results, by adopting both

Integration of biological networks and gene expression data using Cytoscape

PubMed Central

Cline, Melissa S; Smoot, Michael; Cerami, Ethan; Kuchinsky, Allan; Landys, Nerius; Workman, Chris; Christmas, Rowan; Avila-Campilo, Iliana; Creech, Michael; Gross, Benjamin; Hanspers, Kristina; Isserlin, Ruth; Kelley, Ryan; Killcoyne, Sarah; Lotia, Samad; Maere, Steven; Morris, John; Ono, Keiichiro; Pavlovic, Vuk; Pico, Alexander R; Vailaya, Aditya; Wang, Peng-Liang; Adler, Annette; Conklin, Bruce R; Hood, Leroy; Kuiper, Martin; Sander, Chris; Schmulevich, Ilya; Schwikowski, Benno; Warner, Guy J; Ideker, Trey; Bader, Gary D

2013-01-01

Cytoscape is a free software package for visualizing, modeling and analyzing molecular and genetic interaction networks. This protocol explains how to use Cytoscape to analyze the results of mRNA expression profiling, and other functional genomics and proteomics experiments, in the context of an interaction network obtained for genes of interest. Five major steps are described: (i) obtaining a gene or protein network, (ii) displaying the network using layout algorithms, (iii) integrating with gene expression and other functional attributes, (iv) identifying putative complexes and functional modules and (v) identifying enriched Gene Ontology annotations in the network. These steps provide a broad sample of the types of analyses performed by Cytoscape. PMID:17947979

Gene essentiality and the topology of protein interaction networks

PubMed Central

Coulomb, Stéphane; Bauer, Michel; Bernard, Denis; Marsolier-Kergoat, Marie-Claude

2005-01-01

The mechanistic bases for gene essentiality and for cell mutational resistance have long been disputed. The recent availability of large protein interaction databases has fuelled the analysis of protein interaction networks and several authors have proposed that gene dispensability could be strongly related to some topological parameters of these networks. However, many results were based on protein interaction data whose biases were not taken into account. In this article, we show that the essentiality of a gene in yeast is poorly related to the number of interactants (or degree) of the corresponding protein and that the physiological consequences of gene deletions are unrelated to several other properties of proteins in the interaction networks, such as the average degrees of their nearest neighbours, their clustering coefficients or their relative distances. We also found that yeast protein interaction networks lack degree correlation, i.e. a propensity for their vertices to associate according to their degrees. Gene essentiality and more generally cell resistance against mutations thus seem largely unrelated to many parameters of protein network topology. PMID:16087428

Streamflow Forecasting Using Nuero-Fuzzy Inference System

NASA Astrophysics Data System (ADS)

Nanduri, U. V.; Swain, P. C.

2005-12-01

The prediction of flow into a reservoir is fundamental in water resources planning and management. The need for timely and accurate streamflow forecasting is widely recognized and emphasized by many in water resources fraternity. Real-time forecasts of natural inflows to reservoirs are of particular interest for operation and scheduling. The physical system of the river basin that takes the rainfall as an input and produces the runoff is highly nonlinear, complicated and very difficult to fully comprehend. The system is influenced by large number of factors and variables. The large spatial extent of the systems forces the uncertainty into the hydrologic information. A variety of methods have been proposed for forecasting reservoir inflows including conceptual (physical) and empirical (statistical) models (WMO 1994), but none of them can be considered as unique superior model (Shamseldin 1997). Owing to difficulties of formulating reasonable non-linear watershed models, recent attempts have resorted to Neural Network (NN) approach for complex hydrologic modeling. In recent years the use of soft computing in the field of hydrological forecasting is gaining ground. The relatively new soft computing technique of Adaptive Neuro-Fuzzy Inference System (ANFIS), developed by Jang (1993) is able to take care of the non-linearity, uncertainty, and vagueness embedded in the system. It is a judicious combination of the Neural Networks and fuzzy systems. It can learn and generalize highly nonlinear and uncertain phenomena due to the embedded neural network (NN). NN is efficient in learning and generalization, and the fuzzy system mimics the cognitive capability of human brain. Hence, ANFIS can learn the complicated processes involved in the basin and correlate the precipitation to the corresponding discharge. In the present study, one step ahead forecasts are made for ten-daily flows, which are mostly required for short term operational planning of multipurpose reservoirs. A

Integration of multi-omics data for integrative gene regulatory network inference.

PubMed

Zarayeneh, Neda; Ko, Euiseong; Oh, Jung Hun; Suh, Sang; Liu, Chunyu; Gao, Jean; Kim, Donghyun; Kang, Mingon

2017-01-01

Gene regulatory networks provide comprehensive insights and indepth understanding of complex biological processes. The molecular interactions of gene regulatory networks are inferred from a single type of genomic data, e.g., gene expression data in most research. However, gene expression is a product of sequential interactions of multiple biological processes, such as DNA sequence variations, copy number variations, histone modifications, transcription factors, and DNA methylations. The recent rapid advances of high-throughput omics technologies enable one to measure multiple types of omics data, called 'multi-omics data', that represent the various biological processes. In this paper, we propose an Integrative Gene Regulatory Network inference method (iGRN) that incorporates multi-omics data and their interactions in gene regulatory networks. In addition to gene expressions, copy number variations and DNA methylations were considered for multi-omics data in this paper. The intensive experiments were carried out with simulation data, where iGRN's capability that infers the integrative gene regulatory network is assessed. Through the experiments, iGRN shows its better performance on model representation and interpretation than other integrative methods in gene regulatory network inference. iGRN was also applied to a human brain dataset of psychiatric disorders, and the biological network of psychiatric disorders was analysed.

Integration of multi-omics data for integrative gene regulatory network inference

PubMed Central

Zarayeneh, Neda; Ko, Euiseong; Oh, Jung Hun; Suh, Sang; Liu, Chunyu; Gao, Jean; Kim, Donghyun

2017-01-01

Gene regulatory networks provide comprehensive insights and indepth understanding of complex biological processes. The molecular interactions of gene regulatory networks are inferred from a single type of genomic data, e.g., gene expression data in most research. However, gene expression is a product of sequential interactions of multiple biological processes, such as DNA sequence variations, copy number variations, histone modifications, transcription factors, and DNA methylations. The recent rapid advances of high-throughput omics technologies enable one to measure multiple types of omics data, called ‘multi-omics data’, that represent the various biological processes. In this paper, we propose an Integrative Gene Regulatory Network inference method (iGRN) that incorporates multi-omics data and their interactions in gene regulatory networks. In addition to gene expressions, copy number variations and DNA methylations were considered for multi-omics data in this paper. The intensive experiments were carried out with simulation data, where iGRN’s capability that infers the integrative gene regulatory network is assessed. Through the experiments, iGRN shows its better performance on model representation and interpretation than other integrative methods in gene regulatory network inference. iGRN was also applied to a human brain dataset of psychiatric disorders, and the biological network of psychiatric disorders was analysed. PMID:29354189

Fuzzy set methods for object recognition in space applications

NASA Technical Reports Server (NTRS)

Keller, James M.

1991-01-01

Progress on the following tasks is reported: (1) fuzzy set-based decision making methodologies; (2) feature calculation; (3) clustering for curve and surface fitting; and (4) acquisition of images. The general structure for networks based on fuzzy set connectives which are being used for information fusion and decision making in space applications is described. The structure and training techniques for such networks consisting of generalized means and gamma-operators are described. The use of other hybrid operators in multicriteria decision making is currently being examined. Numerous classical features on image regions such as gray level statistics, edge and curve primitives, texture measures from cooccurrance matrix, and size and shape parameters were implemented. Several fractal geometric features which may have a considerable impact on characterizing cluttered background, such as clouds, dense star patterns, or some planetary surfaces, were used. A new approach to a fuzzy C-shell algorithm is addressed. NASA personnel are in the process of acquiring suitable simulation data and hopefully videotaped actual shuttle imagery. Photographs have been digitized to use in the algorithms. Also, a model of the shuttle was assembled and a mechanism to orient this model in 3-D to digitize for experiments on pose estimation is being constructed.

Multimodality Inferring of Human Cognitive States Based on Integration of Neuro-Fuzzy Network and Information Fusion Techniques

NASA Astrophysics Data System (ADS)

Yang, G.; Lin, Y.; Bhattacharya, P.

2007-12-01

To achieve an effective and safe operation on the machine system where the human interacts with the machine mutually, there is a need for the machine to understand the human state, especially cognitive state, when the human's operation task demands an intensive cognitive activity. Due to a well-known fact with the human being, a highly uncertain cognitive state and behavior as well as expressions or cues, the recent trend to infer the human state is to consider multimodality features of the human operator. In this paper, we present a method for multimodality inferring of human cognitive states by integrating neuro-fuzzy network and information fusion techniques. To demonstrate the effectiveness of this method, we take the driver fatigue detection as an example. The proposed method has, in particular, the following new features. First, human expressions are classified into four categories: (i) casual or contextual feature, (ii) contact feature, (iii) contactless feature, and (iv) performance feature. Second, the fuzzy neural network technique, in particular Takagi-Sugeno-Kang (TSK) model, is employed to cope with uncertain behaviors. Third, the sensor fusion technique, in particular ordered weighted aggregation (OWA), is integrated with the TSK model in such a way that cues are taken as inputs to the TSK model, and then the outputs of the TSK are fused by the OWA which gives outputs corresponding to particular cognitive states under interest (e.g., fatigue). We call this method TSK-OWA. Validation of the TSK-OWA, performed in the Northeastern University vehicle drive simulator, has shown that the proposed method is promising to be a general tool for human cognitive state inferring and a special tool for the driver fatigue detection.

Paper-based Synthetic Gene Networks

PubMed Central

Pardee, Keith; Green, Alexander A.; Ferrante, Tom; Cameron, D. Ewen; DaleyKeyser, Ajay; Yin, Peng; Collins, James J.

2014-01-01

Synthetic gene networks have wide-ranging uses in reprogramming and rewiring organisms. To date, there has not been a way to harness the vast potential of these networks beyond the constraints of a laboratory or in vivo environment. Here, we present an in vitro paper-based platform that provides a new venue for synthetic biologists to operate, and a much-needed medium for the safe deployment of engineered gene circuits beyond the lab. Commercially available cell-free systems are freeze-dried onto paper, enabling the inexpensive, sterile and abiotic distribution of synthetic biology-based technologies for the clinic, global health, industry, research and education. For field use, we create circuits with colorimetric outputs for detection by eye, and fabricate a low-cost, electronic optical interface. We demonstrate this technology with small molecule and RNA actuation of genetic switches, rapid prototyping of complex gene circuits, and programmable in vitro diagnostics, including glucose sensors and strain-specific Ebola virus sensors. PMID:25417167

Paper-based synthetic gene networks.

PubMed

Pardee, Keith; Green, Alexander A; Ferrante, Tom; Cameron, D Ewen; DaleyKeyser, Ajay; Yin, Peng; Collins, James J

2014-11-06

Synthetic gene networks have wide-ranging uses in reprogramming and rewiring organisms. To date, there has not been a way to harness the vast potential of these networks beyond the constraints of a laboratory or in vivo environment. Here, we present an in vitro paper-based platform that provides an alternate, versatile venue for synthetic biologists to operate and a much-needed medium for the safe deployment of engineered gene circuits beyond the lab. Commercially available cell-free systems are freeze dried onto paper, enabling the inexpensive, sterile, and abiotic distribution of synthetic-biology-based technologies for the clinic, global health, industry, research, and education. For field use, we create circuits with colorimetric outputs for detection by eye and fabricate a low-cost, electronic optical interface. We demonstrate this technology with small-molecule and RNA actuation of genetic switches, rapid prototyping of complex gene circuits, and programmable in vitro diagnostics, including glucose sensors and strain-specific Ebola virus sensors.

NetDecoder: a network biology platform that decodes context-specific biological networks and gene activities.

PubMed

da Rocha, Edroaldo Lummertz; Ung, Choong Yong; McGehee, Cordelia D; Correia, Cristina; Li, Hu

2016-06-02

The sequential chain of interactions altering the binary state of a biomolecule represents the 'information flow' within a cellular network that determines phenotypic properties. Given the lack of computational tools to dissect context-dependent networks and gene activities, we developed NetDecoder, a network biology platform that models context-dependent information flows using pairwise phenotypic comparative analyses of protein-protein interactions. Using breast cancer, dyslipidemia and Alzheimer's disease as case studies, we demonstrate NetDecoder dissects subnetworks to identify key players significantly impacting cell behaviour specific to a given disease context. We further show genes residing in disease-specific subnetworks are enriched in disease-related signalling pathways and information flow profiles, which drive the resulting disease phenotypes. We also devise a novel scoring scheme to quantify key genes-network routers, which influence many genes, key targets, which are influenced by many genes, and high impact genes, which experience a significant change in regulation. We show the robustness of our results against parameter changes. Our network biology platform includes freely available source code (http://www.NetDecoder.org) for researchers to explore genome-wide context-dependent information flow profiles and key genes, given a set of genes of particular interest and transcriptome data. More importantly, NetDecoder will enable researchers to uncover context-dependent drug targets. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Novel candidate genes important for asthma and hypertension comorbidity revealed from associative gene networks.

PubMed

Saik, Olga V; Demenkov, Pavel S; Ivanisenko, Timofey V; Bragina, Elena Yu; Freidin, Maxim B; Goncharova, Irina A; Dosenko, Victor E; Zolotareva, Olga I; Hofestaedt, Ralf; Lavrik, Inna N; Rogaev, Evgeny I; Ivanisenko, Vladimir A

2018-02-13

Hypertension and bronchial asthma are a major issue for people's health. As of 2014, approximately one billion adults, or ~ 22% of the world population, have had hypertension. As of 2011, 235-330 million people globally have been affected by asthma and approximately 250,000-345,000 people have died each year from the disease. The development of the effective treatment therapies against these diseases is complicated by their comorbidity features. This is often a major problem in diagnosis and their treatment. Hence, in this study the bioinformatical methodology for the analysis of the comorbidity of these two diseases have been developed. As such, the search for candidate genes related to the comorbid conditions of asthma and hypertension can help in elucidating the molecular mechanisms underlying the comorbid condition of these two diseases, and can also be useful for genotyping and identifying new drug targets. Using ANDSystem, the reconstruction and analysis of gene networks associated with asthma and hypertension was carried out. The gene network of asthma included 755 genes/proteins and 62,603 interactions, while the gene network of hypertension - 713 genes/proteins and 45,479 interactions. Two hundred and five genes/proteins and 9638 interactions were shared between asthma and hypertension. An approach for ranking genes implicated in the comorbid condition of two diseases was proposed. The approach is based on nine criteria for ranking genes by their importance, including standard methods of gene prioritization (Endeavor, ToppGene) as well as original criteria that take into account the characteristics of an associative gene network and the presence of known polymorphisms in the analysed genes. According to the proposed approach, the genes IL10, TLR4, and CAT had the highest priority in the development of comorbidity of these two diseases. Additionally, it was revealed that the list of top genes is enriched with apoptotic genes and genes involved in

Reliable design of a closed loop supply chain network under uncertainty: An interval fuzzy possibilistic chance-constrained model

NASA Astrophysics Data System (ADS)

Vahdani, Behnam; Tavakkoli-Moghaddam, Reza; Jolai, Fariborz; Baboli, Arman

2013-06-01

This article seeks to offer a systematic approach to establishing a reliable network of facilities in closed loop supply chains (CLSCs) under uncertainties. Facilities that are located in this article concurrently satisfy both traditional objective functions and reliability considerations in CLSC network designs. To attack this problem, a novel mathematical model is developed that integrates the network design decisions in both forward and reverse supply chain networks. The model also utilizes an effective reliability approach to find a robust network design. In order to make the results of this article more realistic, a CLSC for a case study in the iron and steel industry has been explored. The considered CLSC is multi-echelon, multi-facility, multi-product and multi-supplier. Furthermore, multiple facilities exist in the reverse logistics network leading to high complexities. Since the collection centres play an important role in this network, the reliability concept of these facilities is taken into consideration. To solve the proposed model, a novel interactive hybrid solution methodology is developed by combining a number of efficient solution approaches from the recent literature. The proposed solution methodology is a bi-objective interval fuzzy possibilistic chance-constraint mixed integer linear programming (BOIFPCCMILP). Finally, computational experiments are provided to demonstrate the applicability and suitability of the proposed model in a supply chain environment and to help decision makers facilitate their analyses.

Pleiotropic and Epistatic Network-Based Discovery: Integrated Networks for Target Gene Discovery

DOE Office of Scientific and Technical Information (OSTI.GOV)

Weighill, Deborah; Jones, Piet; Shah, Manesh

Biological organisms are complex systems that are composed of functional networks of interacting molecules and macro-molecules. Complex phenotypes are the result of orchestrated, hierarchical, heterogeneous collections of expressed genomic variants. However, the effects of these variants are the result of historic selective pressure and current environmental and epigenetic signals, and, as such, their co-occurrence can be seen as genome-wide correlations in a number of different manners. Biomass recalcitrance (i.e., the resistance of plants to degradation or deconstruction, which ultimately enables access to a plant's sugars) is a complex polygenic phenotype of high importance to biofuels initiatives. This study makes usemore » of data derived from the re-sequenced genomes from over 800 different Populus trichocarpa genotypes in combination with metabolomic and pyMBMS data across this population, as well as co-expression and co-methylation networks in order to better understand the molecular interactions involved in recalcitrance, and identify target genes involved in lignin biosynthesis/degradation. A Lines Of Evidence (LOE) scoring system is developed to integrate the information in the different layers and quantify the number of lines of evidence linking genes to target functions. This new scoring system was applied to quantify the lines of evidence linking genes to lignin-related genes and phenotypes across the network layers, and allowed for the generation of new hypotheses surrounding potential new candidate genes involved in lignin biosynthesis in P. trichocarpa, including various AGAMOUS-LIKE genes. Lastly, the resulting Genome Wide Association Study networks, integrated with Single Nucleotide Polymorphism (SNP) correlation, co-methylation, and co-expression networks through the LOE scores are proving to be a powerful approach to determine the pleiotropic and epistatic relationships underlying cellular functions and, as such, the molecular basis for

Pleiotropic and Epistatic Network-Based Discovery: Integrated Networks for Target Gene Discovery

DOE PAGES

Weighill, Deborah; Jones, Piet; Shah, Manesh; ...

2018-05-11

Biological organisms are complex systems that are composed of functional networks of interacting molecules and macro-molecules. Complex phenotypes are the result of orchestrated, hierarchical, heterogeneous collections of expressed genomic variants. However, the effects of these variants are the result of historic selective pressure and current environmental and epigenetic signals, and, as such, their co-occurrence can be seen as genome-wide correlations in a number of different manners. Biomass recalcitrance (i.e., the resistance of plants to degradation or deconstruction, which ultimately enables access to a plant's sugars) is a complex polygenic phenotype of high importance to biofuels initiatives. This study makes usemore » of data derived from the re-sequenced genomes from over 800 different Populus trichocarpa genotypes in combination with metabolomic and pyMBMS data across this population, as well as co-expression and co-methylation networks in order to better understand the molecular interactions involved in recalcitrance, and identify target genes involved in lignin biosynthesis/degradation. A Lines Of Evidence (LOE) scoring system is developed to integrate the information in the different layers and quantify the number of lines of evidence linking genes to target functions. This new scoring system was applied to quantify the lines of evidence linking genes to lignin-related genes and phenotypes across the network layers, and allowed for the generation of new hypotheses surrounding potential new candidate genes involved in lignin biosynthesis in P. trichocarpa, including various AGAMOUS-LIKE genes. Lastly, the resulting Genome Wide Association Study networks, integrated with Single Nucleotide Polymorphism (SNP) correlation, co-methylation, and co-expression networks through the LOE scores are proving to be a powerful approach to determine the pleiotropic and epistatic relationships underlying cellular functions and, as such, the molecular basis for

Inferring Time-Varying Network Topologies from Gene Expression Data

PubMed Central

2007-01-01

Most current methods for gene regulatory network identification lead to the inference of steady-state networks, that is, networks prevalent over all times, a hypothesis which has been challenged. There has been a need to infer and represent networks in a dynamic, that is, time-varying fashion, in order to account for different cellular states affecting the interactions amongst genes. In this work, we present an approach, regime-SSM, to understand gene regulatory networks within such a dynamic setting. The approach uses a clustering method based on these underlying dynamics, followed by system identification using a state-space model for each learnt cluster—to infer a network adjacency matrix. We finally indicate our results on the mouse embryonic kidney dataset as well as the T-cell activation-based expression dataset and demonstrate conformity with reported experimental evidence. PMID:18309363

Inferring time-varying network topologies from gene expression data.

PubMed

Rao, Arvind; Hero, Alfred O; States, David J; Engel, James Douglas

2007-01-01

Most current methods for gene regulatory network identification lead to the inference of steady-state networks, that is, networks prevalent over all times, a hypothesis which has been challenged. There has been a need to infer and represent networks in a dynamic, that is, time-varying fashion, in order to account for different cellular states affecting the interactions amongst genes. In this work, we present an approach, regime-SSM, to understand gene regulatory networks within such a dynamic setting. The approach uses a clustering method based on these underlying dynamics, followed by system identification using a state-space model for each learnt cluster--to infer a network adjacency matrix. We finally indicate our results on the mouse embryonic kidney dataset as well as the T-cell activation-based expression dataset and demonstrate conformity with reported experimental evidence.

Travel Time Estimation Using Freeway Point Detector Data Based on Evolving Fuzzy Neural Inference System.

PubMed

Tang, Jinjun; Zou, Yajie; Ash, John; Zhang, Shen; Liu, Fang; Wang, Yinhai

2016-01-01

Travel time is an important measurement used to evaluate the extent of congestion within road networks. This paper presents a new method to estimate the travel time based on an evolving fuzzy neural inference system. The input variables in the system are traffic flow data (volume, occupancy, and speed) collected from loop detectors located at points both upstream and downstream of a given link, and the output variable is the link travel time. A first order Takagi-Sugeno fuzzy rule set is used to complete the inference. For training the evolving fuzzy neural network (EFNN), two learning processes are proposed: (1) a K-means method is employed to partition input samples into different clusters, and a Gaussian fuzzy membership function is designed for each cluster to measure the membership degree of samples to the cluster centers. As the number of input samples increases, the cluster centers are modified and membership functions are also updated; (2) a weighted recursive least squares estimator is used to optimize the parameters of the linear functions in the Takagi-Sugeno type fuzzy rules. Testing datasets consisting of actual and simulated data are used to test the proposed method. Three common criteria including mean absolute error (MAE), root mean square error (RMSE), and mean absolute relative error (MARE) are utilized to evaluate the estimation performance. Estimation results demonstrate the accuracy and effectiveness of the EFNN method through comparison with existing methods including: multiple linear regression (MLR), instantaneous model (IM), linear model (LM), neural network (NN), and cumulative plots (CP).

Travel Time Estimation Using Freeway Point Detector Data Based on Evolving Fuzzy Neural Inference System

PubMed Central

Tang, Jinjun; Zou, Yajie; Ash, John; Zhang, Shen; Liu, Fang; Wang, Yinhai

2016-01-01

Travel time is an important measurement used to evaluate the extent of congestion within road networks. This paper presents a new method to estimate the travel time based on an evolving fuzzy neural inference system. The input variables in the system are traffic flow data (volume, occupancy, and speed) collected from loop detectors located at points both upstream and downstream of a given link, and the output variable is the link travel time. A first order Takagi-Sugeno fuzzy rule set is used to complete the inference. For training the evolving fuzzy neural network (EFNN), two learning processes are proposed: (1) a K-means method is employed to partition input samples into different clusters, and a Gaussian fuzzy membership function is designed for each cluster to measure the membership degree of samples to the cluster centers. As the number of input samples increases, the cluster centers are modified and membership functions are also updated; (2) a weighted recursive least squares estimator is used to optimize the parameters of the linear functions in the Takagi-Sugeno type fuzzy rules. Testing datasets consisting of actual and simulated data are used to test the proposed method. Three common criteria including mean absolute error (MAE), root mean square error (RMSE), and mean absolute relative error (MARE) are utilized to evaluate the estimation performance. Estimation results demonstrate the accuracy and effectiveness of the EFNN method through comparison with existing methods including: multiple linear regression (MLR), instantaneous model (IM), linear model (LM), neural network (NN), and cumulative plots (CP). PMID:26829639

Modulation transfer function estimation of optical lens system by adaptive neuro-fuzzy methodology

NASA Astrophysics Data System (ADS)

Petković, Dalibor; Shamshirband, Shahaboddin; Pavlović, Nenad T.; Anuar, Nor Badrul; Kiah, Miss Laiha Mat

2014-07-01

The quantitative assessment of image quality is an important consideration in any type of imaging system. The modulation transfer function (MTF) is a graphical description of the sharpness and contrast of an imaging system or of its individual components. The MTF is also known and spatial frequency response. The MTF curve has different meanings according to the corresponding frequency. The MTF of an optical system specifies the contrast transmitted by the system as a function of image size, and is determined by the inherent optical properties of the system. In this study, the adaptive neuro-fuzzy (ANFIS) estimator is designed and adapted to estimate MTF value of the actual optical system. Neural network in ANFIS adjusts parameters of membership function in the fuzzy logic of the fuzzy inference system. The back propagation learning algorithm is used for training this network. This intelligent estimator is implemented using Matlab/Simulink and the performances are investigated. The simulation results presented in this paper show the effectiveness of the developed method.

Inference of cancer-specific gene regulatory networks using soft computing rules.

PubMed

Wang, Xiaosheng; Gotoh, Osamu

2010-03-24

Perturbations of gene regulatory networks are essentially responsible for oncogenesis. Therefore, inferring the gene regulatory networks is a key step to overcoming cancer. In this work, we propose a method for inferring directed gene regulatory networks based on soft computing rules, which can identify important cause-effect regulatory relations of gene expression. First, we identify important genes associated with a specific cancer (colon cancer) using a supervised learning approach. Next, we reconstruct the gene regulatory networks by inferring the regulatory relations among the identified genes, and their regulated relations by other genes within the genome. We obtain two meaningful findings. One is that upregulated genes are regulated by more genes than downregulated ones, while downregulated genes regulate more genes than upregulated ones. The other one is that tumor suppressors suppress tumor activators and activate other tumor suppressors strongly, while tumor activators activate other tumor activators and suppress tumor suppressors weakly, indicating the robustness of biological systems. These findings provide valuable insights into the pathogenesis of cancer.

Global sensitivity analysis for fuzzy inputs based on the decomposition of fuzzy output entropy

NASA Astrophysics Data System (ADS)

Shi, Yan; Lu, Zhenzhou; Zhou, Yicheng

2018-06-01

To analyse the component of fuzzy output entropy, a decomposition method of fuzzy output entropy is first presented. After the decomposition of fuzzy output entropy, the total fuzzy output entropy can be expressed as the sum of the component fuzzy entropy contributed by fuzzy inputs. Based on the decomposition of fuzzy output entropy, a new global sensitivity analysis model is established for measuring the effects of uncertainties of fuzzy inputs on the output. The global sensitivity analysis model can not only tell the importance of fuzzy inputs but also simultaneously reflect the structural composition of the response function to a certain degree. Several examples illustrate the validity of the proposed global sensitivity analysis, which is a significant reference in engineering design and optimization of structural systems.

Fuzzy topological digital space and digital fuzzy spline of electroencephalography during epileptic seizures

NASA Astrophysics Data System (ADS)

Shah, Mazlina Muzafar; Wahab, Abdul Fatah

2017-08-01

Epilepsy disease occurs because of there is a temporary electrical disturbance in a group of brain cells (nurons). The recording of electrical signals come from the human brain which can be collected from the scalp of the head is called Electroencephalography (EEG). EEG then considered in digital format and in fuzzy form makes it a fuzzy digital space data form. The purpose of research is to identify the area (curve and surface) in fuzzy digital space affected by inside epilepsy seizure in epileptic patient's brain. The main focus for this research is to generalize fuzzy topological digital space, definition and basic operation also the properties by using digital fuzzy set and the operations. By using fuzzy digital space, the theory of digital fuzzy spline can be introduced to replace grid data that has been use previously to get better result. As a result, the flat of EEG can be fuzzy topological digital space and this type of data can be use to interpolate the digital fuzzy spline.

Fuzzy logic controller optimization

DOEpatents

Sepe, Jr., Raymond B; Miller, John Michael

2004-03-23

A method is provided for optimizing a rotating induction machine system fuzzy logic controller. The fuzzy logic controller has at least one input and at least one output. Each input accepts a machine system operating parameter. Each output produces at least one machine system control parameter. The fuzzy logic controller generates each output based on at least one input and on fuzzy logic decision parameters. Optimization begins by obtaining a set of data relating each control parameter to at least one operating parameter for each machine operating region. A model is constructed for each machine operating region based on the machine operating region data obtained. The fuzzy logic controller is simulated with at least one created model in a feedback loop from a fuzzy logic output to a fuzzy logic input. Fuzzy logic decision parameters are optimized based on the simulation.