Neufuz: neural network based fuzzy logic design algorithms

Khan, E.; Venkatapuram, P.

doi:10.1109/fuzzy.1993.327412

Cited by 29 publications

(14 citation statements)

References 4 publications

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“…Inspired by neural networks, researchers [32,33,34] have set up connective models of a fuzzy system. Common optimization techniques in neural network theory, such a^ gradient search (sJso called bacJtpropagation) and conjugate search have been used.…”

Section: Neural Network Approachmentioning

confidence: 99%

Automatic Optimal Design of Fuzzy Systems Based on Universal Approximation and Evolutionary Programming

Nyberg¹,

Pao²

1995

Fuzzy Logic and Intelligent Systems

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Section: Neural Network Approachmentioning

confidence: 99%

Automatic Optimal Design of Fuzzy Systems Based on Universal Approximation and Evolutionary Programming

Nyberg¹,

Pao²

1995

Fuzzy Logic and Intelligent Systems

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“…This is equivalent to finding a hyperbox (category) to which could belong. The chosen category is indexed by where (12) Resonance occurs when the match value of the chosen category meets the vigilance criterion (13) where is a vigilance parameter. If the vigilance criterion is not met we say mismatch reset occurs.…”

Section: A the Structure Learning Stepmentioning

confidence: 99%

“…A new index is then chosen using (12). The search process continues until the chosen satisfies (13). If no such is found, then a new input hyperbox is created by adding a set of new input-term nodes, one for each input-linguistic variable, and setting up links between the newly added inputterm nodes and the input-linguistic nodes.…”

Section: A the Structure Learning Stepmentioning

confidence: 99%

An ART-based fuzzy adaptive learning control network

Lin

1997

IEEE Trans. Fuzzy Syst.

186

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This paper addresses the structure and an associated on-line learning algorithm of a feedforward multilayered connectionist network for realizing the basic elements and functions of a traditional fuzzy logic controller. The proposed fuzzy adaptive learning control network (FALCON) can be contrasted with the traditional fuzzy logic control systems in their network structure and learning ability. An on-line structure/parameter learning algorithm called FALCON-ART is proposed for constructing the FALCON dynamically. It combines the backpropagation learning scheme for parameter learning and the fuzzy ART algorithm for structure learning. The FALCON-ART has some important features. First of all, it partitions the input state space and output control space using irregular fuzzy hyperboxes according to the distribution of training data. In many existing fuzzy or neural fuzzy control systems, the input and output spaces are always partitioned into "grids." As the number of input/output variables increases, the number of partitioned grids will grow combinatorially. To avoid the problem of combinatorial growing of partitioned grids in some complex systems, the FALCON-ART partitions the input/output spaces in a flexible way based on the distribution of training data. Second, the FALCON-ART can create and train the 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 even any initial information on these. More notably, the FALCON-ART can on-line partition the input/output spaces, tune membership functions, find proper fuzzy logic rules, and annihilate redundant rules dynamically upon receiving online incoming training data. Computer simulations have been conducted to illustrate the performance and applicability of the proposed system.

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“…As far as we know, none of these algorithms can deal with more than five input variables. For example, the well-known fuzzy logic design kit NeuFuz4, by Kahn et al [17]- [19], can only solve the problem of at most, four variables. Even for very limited input variables, most of them can only deal with "straight" membership functions, such as trapezoidal-shaped membership functions, so that the resulting controllers represent a linear control.…”

Section: Introductionmentioning

confidence: 99%

An efficient algorithm for automatically generating multivariable fuzzy systems by Fourier series method

Chen

Tokuda²

2002

IEEE Trans. Syst., Man, Cybern. B

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By exploiting the Fourier series expansion, we have developed a new constructive method of automatically generating a multivariable fuzzy inference system from any given sample set with the resulting multivariable function being constructed within any specified precision to the original sample set. The given sample sets are first decomposed into a cluster of simpler sample sets such that a single input fuzzy system is constructed readily for a sample set extracted directly from the cluster independent of the other variables. Once the relevant fuzzy rules and membership functions are constructed for each of the variables completely independent of the other variables, the resulting decomposed fuzzy rules and membership functions are integrated back into the fuzzy system appropriate for the original sample set requiring only a moderate cost of computation in the required decomposition and composition processes. After proving two basic theorems which we need to ensure the validity of the decomposition and composition processes of the system construction, we have demonstrated a constructive algorithm of a multivariable fuzzy system. Exploiting an implicit error bound analysis available at each of the construction steps, the present Fourier method is capable of implementing a more stable fuzzy system than the power series expansion method of ParNeuFuz and PolyNeuFuz, covering and implementing a wider range of more robust applications.

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Neufuz: neural network based fuzzy logic design algorithms

Cited by 29 publications

References 4 publications

Automatic Optimal Design of Fuzzy Systems Based on Universal Approximation and Evolutionary Programming

Automatic Optimal Design of Fuzzy Systems Based on Universal Approximation and Evolutionary Programming

An ART-based fuzzy adaptive learning control network

An efficient algorithm for automatically generating multivariable fuzzy systems by Fourier series method

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