evolving fuzzy neural network
Evolving Intelligent Systems, eIS
... emerging evolving systems paradigm mimics the evolution of individuals in the Nature during their life-cycle, especially humans: learning from experience, inheritance, gradual change, knowledge generation from ...
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Interval type 2 intuitionistic fuzzy logic system for non linear system prediction
... three evolving T2FLSs namely, self evolving interval type-2 fuzzy neural network (SEIT2FNN) utilising IT2FS in the antecedents and TSK interval type-1 set in the consequent, ...
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A Study & Survey on Rainfall Prediction And Production of Crops Using Data Mining Techniques
... of evolving neuro-fuzzy structures techniques can be observed in and Pseudo outer-product-based fuzzy neural networks ("POPFNN") are a family of neuro-fuzzy systems that are ...
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Fuzzy Neural Network for Clustering and Classification
... days fuzzy logic and neural network are greatly used to develop intelligent systems ...combine fuzzy logic and neural network is that fuzzy logic have greater ability to ...
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Neural Network Regressions with Fuzzy Clustering
... and fuzzy clustering. Their main idea is to employ a fuzzy clustering for linear or polynomial regression models for data ...optimal fuzzy membership values for each object such that the overall ...
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Artificial Intelligence and its Application as an Integrated Approach
... AlphaGo has revolutionized the AI space with the most recent feat that involved beating top Go player, Lee Sedol. AlphaGo essentially uses a Monte Carlo tree search to base its move upon previously “learned” knowledge ...
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Title: Evolving Neural Network for Kernel Principal Component Analysis
... of evolving connectionist systems [20], which implies not only the tunning of synaptic weights of the neural network, but also its architecture directly in the learning ...[7] evolving ...
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Fuzzy inference systems implemented on neural architectures for motor fault detection and diagnosis
... This paper has described and illustrated the application of two popular NN/FZ systems, the AFD and the FFD, for motor fault detection/diagnosis. Both structures can provide good fault detection/diagnosis under varying ...
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FLEXIBLE SHARING IN DHT BASED P2P NETWORKS USING METADATA OF RESOURCE
... introduce fuzzy neural network’s basic principle; secondly to use 60 days numerical weather prediction (NWP) data and power data, from ...of fuzzy neural network to train the ...
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Neuro-Fuzzy Scheduler for the Control of Real Time Spherical Tank Process
... In fuzzy clustering, the data points can belong to more than one cluster, and associated with each of the points are membership grades, which indicate the degree to which the data points belong to the different ...
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Deduction of reservoir operating rules for application in global hydrological models
... two fuzzy rules (see Fig. 9b). The training of the network for Charvak takes a lot longer than for Andijan, with more than 200 epochs, although the difference in error is minimal as seen in ...
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Review Computer Network Routing with a Fuzzy Neural Network
... applied fuzzy reasoning enhanced by a neural ...computer network greatly affects the travel time for all types of ...computer network can delay or completely stop the transmission of ...
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A chemical-reaction-optimization-based neuro-fuzzy hybrid network for stock closing price prediction
... function is found to be better. The Gaussian membership function enables smoother transitions between members and non-members in comparison to triangular and trap- ezoidal membership functions, and it has fewer ...
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Neural Network Design - Free Computer, Programming, Mathematics, Technical Books, Lecture Notes and Tutorials
... The brain consists of a large number (approximately 10 11 ) of highly con- nected elements (approximately 10 4 connections per element) called neu- rons. For our purposes these neurons have three principal components: ...
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Research of Planetary Gear Fault Diagnosis Based on Permutation Entropy of CEEMDAN and ANFIS
... the fuzzy reasoning process, the fuzzy rules and membership function parameters in the model can be adjusted adaptively, which makes the model have high prediction accuracy, and the algorithm can be ...
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Implementing The Speaker Recognition Process In Python Using ‘Thuyg-20-Sre’ Data Set
... Processing elements are interconnected with each other and performs a gradient descent methods. These processing components is considered as “neurons”. The skilled or the competent neural networks allows the ...
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Solving fully fuzzy linear programming
... Linear programming is concerned with the opti- mization (minimization or maximization) of a lin- ear function while satisfying a set of linear equal- ity and/ or inequality constrains or restrictions. In the real life ...
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Online Full Text
... a fuzzy compensator is verified by some simulations. The fuzzy compensation controller design uses a simple fuzzy system can remove completely the chattering ...
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The Most General Intelligent Architectures of the Hybrid Neuro-Fuzzy Models
... the network adjusting at a certain level the fuzzy system remains without any ...specific fuzzy system ...neuro fuzzy systems can be classified into four types: 1-The cooperative ...
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Correntropy Based Evolving Fuzzy Neural System
... International Neural Networks Society and a member of the Board of Governors of the Systems, Man and Cybernetics Society of the IEEE, a Distinguished Lecturer of ...the Evolving Systems journal (Springer) ...
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