Cheng-Jian Lin

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This study presents an evolutionary neural fuzzy network, designed using the functional-link-based neural fuzzy network (FLNFN) and a new evolutionary learning algorithm. This new evolutionary learning algorithm is based on a hybrid of cooperative particle swarm optimization and cultural algorithm. It is thus called cultural cooperative particle swarm(More)
The brain–computer interface (BCI) is a system that transforms the brain activity of different mental tasks into a control signal. The system provides an augmentative communication method for patients with severe motor disabilities. In this paper, a neural classifier based on improved particle swarm optimization (IPSO) is proposed to classify an(More)
This paper presents a wavelet-based recurrent fuzzy neural network (WRFNN) for prediction and identification of nonlinear dynamic systems. The proposed WRFNN model combines the traditional Takagi-Sugeno-Kang (TSK) fuzzy model and the wavelet neural networks (WNN). This paper adopts the nonorthogonal and compactly supported functions as wavelet neural(More)
In this paper, we propose a self-adaptive neural fuzzy network with group-based symbiotic evolution (SANFN-GSE) method. A self-adaptive learning algorithm consists of two major components. First, a self-clustering algorithm (SCA) identifies a parsimonious internal structure. An internal structure is said to be parsimonious in the sense that the number of(More)
This paper proposes a reinforcement fuzzy adaptive learning control network (RFALCON), constructed by integrating two fuzzy adaptive learning control networks (FALCON), each of which has a feedforward multilayer network and is developed for the realization of a fuzzy controller. One FALCON performs as a critic network (fuzzy predictor), the other as an(More)
In this paper, a TSK-type fuzzy model (TFM) with a hybrid evolutionary learning algorithm (HELA) is proposed. The proposed HELA method combines the compact genetic algorithm (CGA) and the modified variable-length genetic algorithm (MVGA). Both the number of fuzzy rules and the adjustable parameters in the TFM are designed concurrently by the HELA method. In(More)
This study presents an adaptive neural fuzzy network (ANFN) controller based on a modified differential evolution (MODE) for solving control problems. The proposed ANFN controller adopts a functional link neural network as the consequent part of the fuzzy rules. Thus, the consequent part of the ANFN controller is a nonlinear combination of input variables.(More)