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Temporal problems solved by dynamic fuzzy network based on genetic algorithm with variable-length chromosomes
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AI
DOI：10.1016/S0165-0114(03)00119-2.png)
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Abstract
En 中文
In this paper, a dynamic fuzzy network and its design based on genetic algorithm with variable-length chromosomes is proposed. First, the dynamic fuzzy network constituted from a series of dynamic fuzzy if-then rules is proposed. One characteristic of this network is its ability to deal with temporal problems. Then, the proposed genetic algorithm with variable-length chromosomes is adopted into the design process as a means of allowing the application of the network in situations where the actual desired output is unavailable. In the proposed genetic algorithm, the length of each chromosome varies with the number of rules coded in it. Using this algorithm, no pre-assignment of the number of rules in the dynamic fuzzy network is required, since it can always help to find the most suitable number of rules. All free parameters in the network, including the spatial input partition, consequent parameters and feedback connection weights, are tuned concurrently. To further promote the design performance, genetic algorithm with variable-length chromosomes and relative-based mutated reproduction operation is proposed. In this algorithm, the elite individuals are directly reproduced to the next generation only when their averaged similarity value is smaller than a similarity threshold; otherwise, the elites are mutated to the next generation. To show the efficiency of this dynamic fuzzy network designed by genetic algorithm with variable-length chromosomes and relative-based mutated reproduction operation, two temporal problems are simulated. The simulated results and comparisons with recurrent neural and fuzzy networks verify the efficacy and efficiency of the proposed approach. (C) 2003 Elsevier B.V. All rights reserved.
Keywords:
flexible partition
recurrent neural network
neuro-fuzzy systems
dynamic plant control
adaptive noise cancellation
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