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A novel algorithm, bases on the adaptive resonance theory (ART), is proposed to design the structure of radial basis function (RBF) neural networks in this paper. Based on the concept of “similarity”, this proposed ART-like algorithm can be utilized to construct the RBF neural network. The number of the hidden nodes is able to be adjusted in the learning process. Meanwhile, the activity of each hidden...
In this paper, a novel kind of the dissolved oxygen (DO) concentration control system was proposed based on the T-S fuzzy neural network. The proposed T-S fuzzy neural network controller was used to control the DO concentration in the Benchmark Simulation Model No.1 (BSM1) wastewater treatment platform. The parameters of the neural network were adjusted online through the error back propagation algorithm...
A collaborative optimization model is proposed to integrate the energy performance to the optimization of sintering burden. Firstly, the collaborative optimization problem in sintering burden is described, and then the association model between sinter raw material proportion, production parameter and energy consumption, drum index is provided using data-based methods. Furthermore, objective functions...
In this paper, a novel self-organizing radial basis function (SORBF) neural network is proposed for nonlinear identification and modeling. The proposed SORBF consists of simultaneous network construction and parameter optimization. It offers two important advantages. First, the hidden neurons in the SORBF neural network can be added or removed, based on the neuron activity and mutual information (MI),...
In this paper, cycle time prediction of wafer lots is studied. A memetic algorithm called GSMPSO by combining the PSO with a Gaussian mutation operator and a Simulated Annealing (SA)-based local search operator is developed to weight the features for K Nearest Neighbors (KNN) regression. The GSMPSO-KNN regression method is used to predict the cycle time of wafer lots. The experiment result demonstrates...
This paper integrates the ideas of “divide and conquer” and “put the heads together”, and proposes a method of selecting sub-networks on-line based on fuzzy decision-making. For a given input, this method calculates the distances between the input and the centers of sub-networks firstly, and then obtains the fuzzy member degree based on distance measure, finally realizes on-line sub-networks selection...
Self-balancing two-wheel robot is a high order, multi-variable, nonlinear, strong-coupling and absolutely unstable system. A reinforcement learning algorithm based on many parallel Cerebellar Model Articulation Controller (CMAC) neural networks is proposed for the balance-control problem of self-balancing two-wheel robot. In the method, the outputs of CMAC are used to approximate the Q-functions of...
In this paper, a MISO fuzzy neural network algorithm is presented. This algorithm consists of the excellences of fuzzy algorithm and neural network algorithm. In the parameter learning phase it changes the parameters based on the Lyapunov stability theory to ensure the stability. Meanwhile, it didnpsilat need to seek the whole minimum value when it modifies the parameters. So the algorithm can reach...
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