Journal ArticleDOI
An ART-based fuzzy adaptive learning control network
Cheng-Jian Lin,Chin-Teng Lin +1 more
TLDR
In this paper, an online structure/parameter learning algorithm, FALCON-ART, is proposed for constructing a fuzzy adaptive learning control network (FALCON), which combines backpropagation for parameter learning and fuzzy ART for structure learning.Abstract:
This paper addresses the structure and an associated online learning algorithm of a feedforward multilayer neural net for realizing the basic elements and functions of a fuzzy controller. The proposed fuzzy adaptive learning control network (FALCON) can be contrasted with traditional fuzzy control systems in network structure and learning ability. An online structure/parameter learning algorithm, FALCON-ART, is proposed for constructing FALCON dynamically. It combines backpropagation for parameter learning and fuzzy ART for structure learning. FALCON-ART partitions the input state space and output control space using irregular fuzzy hyperboxes according to the data distribution. In many existing fuzzy or neural fuzzy control systems, the input and output spaces are always partitioned into "grids". As the number of variables increases, the number of partitioned grids grows combinatorially. To avoid this problem in some complex systems, FALCON-ART partitions the I/O spaces flexibly based on data distribution. It can create and train FALCON in a highly autonomous way. In its initial form, there is no membership function, fuzzy partition, and fuzzy logic rule. They are created and begin to grow as the first training pattern arrives. Thus, the users need not give it any a priori knowledge or initial information. FALCON-ART can online partition the I/O spaces, tune membership functions, find proper fuzzy logic rules, and annihilate redundant rules dynamically upon receiving online data.read more
Citations
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Journal ArticleDOI
An online self-constructing neural fuzzy inference network and its applications
Chia-Feng Juang,Chin-Teng Lin +1 more
TL;DR: A linear transformation for each input variable can be incorporated into the network so that much fewer rules are needed or higher accuracy can be achieved.
Journal ArticleDOI
A Self-Evolving Interval Type-2 Fuzzy Neural Network With Online Structure and Parameter Learning
Chia-Feng Juang,Yu-Wei Tsao +1 more
TL;DR: This paper proposes a self-evolving interval type-2 fuzzy neural network (SEIT2FNN) with online structure and parameter learning, which is applied to simulations on nonlinear plant modeling, adaptive noise cancellation, and chaotic signal prediction.
Journal ArticleDOI
Support vector learning mechanism for fuzzy rule-based modeling: a new approach
Jung-Hsien Chiang,Pei-Yi Hao +1 more
TL;DR: A rule-based framework that explicitly characterizes the representation in fuzzy inference procedure, which has the inherent advantage that the model does not have to determine the number of rules in advance, and the overall fuzzy inference system can be represented as series expansion of fuzzy basis functions.
Journal ArticleDOI
GenSoFNN: a generic self-organizing fuzzy neural network
W. L. Tung,Chai Quek +1 more
TL;DR: A novel neural fuzzy system that is immune to the above-mentioned deficiencies is proposed in this paper and is named the generic self-organizing fuzzy neural network (GenSoFNN).
Journal ArticleDOI
Identification and Prediction of Dynamic Systems Using an Interactively Recurrent Self-Evolving Fuzzy Neural Network
TL;DR: This paper presents a novel recurrent fuzzy neural network, called an interactively recurrent self-evolving fuzzy Neural Network (IRSFNN), for prediction and identification of dynamic systems and compares it to other well-known recurrent FNNs.
References
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