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New Methods for Designing and Reduction of Neuro-Fuzzy Systems

Cpałka K.

Journal of Applied Computer Science Methods

2010

Vol. 2 No. 2

113--126

In the paper, we propose novel methods for designing and reduction of neuro-fuzzy systems without the deterioration of their accuracy. The reduction and merging algorithms gradually eliminate inputs, rules, antecedents, and the number of discretization points of integrals in the center of area defuzzification method. Our algorithms have been tested using well known classification benchmark.

A neuro-fuzzy controller with a compromise fuzzy reasoning

Rutkowski L., Cpałka K.

Control and Cybernetics

2002

Vol. 31, no 2

297-308

This paper presents a compromise approach to neuro-fuzzy controllers. It includes both Mamdani (constructive) and logical (destructive) fuzzy inference. New neuro-fuzzy controllers are derived and simulation results are presented.

Implication-Based Neuro-Fuzzy Architectures

Rutkowska D., Nowicki R.

International Journal of Applied Mathematics and Computer Science

2000

Vol. 10, no 4

675-701

This paper presents connectionist multi-layer architectures of neuro-fuzzy systems based on various fuzzy implications. The well-known Mamdani approach (constructive) and the logical approach (destructive) are considered. Two kinds of architectures, a simpler and a more general one, are distinguished. Examples of application to classification and control problems are provided.