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This paper proposes a recurrent neural fuzzy network with the reinforcement improved particle swarm optimization (R-IPSO) for solving various control problems. The R-IPSO, which consists of structure learning and parameter learning, is also proposed. The structure learning is adopts several sub-swarms to constitute variable fuzzy systems and uses an elite-based structure strategy (ESS) to find suitable...
This paper presents a method of continuous-time simple adaptive control (SAC) using neural network based on genetic algorithm (GA) for a single-input single-output (SISO) nonlinear systems, bounded-input bounded-output, and bounded nonlinearities. According to the power of neural network and the characteristics of simple adaptive control, constructed a simple adaptive control using neural networks,...
The paper discusses the quadratic neural unit (QNU) and highlights its attractiveness for industrial applications such as for plant modeling, control, and time series prediction. Linear systems are still often preferred in industrial control applications for their solvable and single solution nature and for the clarity to the most application engineers. Artificial neural networks are powerful cognitive...
A Bayesian-Gaussian neural network (BGNN) method for nonlinear time variation system identification is proposed in this article. In the redefined BGNN training algorithms, the threshold matrix parameters are optimized by the swarm intelligence optimization algorithm(s) off-line and the sliding window data method are adopted for the BGNN on-line prediction. Some typical time-variation nonlinear systems...
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