An introduction to radial basis functions for system identification. A comparison with other neural network methods

Warwick, Kevin; Craddock, R.

doi:10.1109/cdc.1996.574355

Cited by 26 publications

(11 citation statements)

References 19 publications

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“…However, there are faced with several drawbacks such as potentially preconverging to a local minima, relatively slow convergence rate, and difficulties to determine an adequate architecture to obtain a minimum [12].…”

Section: A Rbf Neural Networkmentioning

confidence: 99%

Design of online soft sensors based on combined adaptive PCA and RBF neural networks

Salahshoor¹,

Kordestani²,

Khoshro³

2009

2009 IEEE Symposium on Computational Intelligence in Control and Automation

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An accurate on-line measurement of important quality variables is essential for successful monitoring and controlling of chemical processes. However, these variables are usually difficult to measure on-line due to the practical limitations such as the time-delay, high cost and reliability considerations. To overcome this problem, two online soft sensors are proposed based upon a combined adaptive principal component analysis (PCA) and a radial basis functions (RBF) artificial neural network. For this purpose, a recursive PCA and a PCA based on a sliding window scheme are presented to adaptively extract the inherent features inside the measurements with high dimensions. The extracted low-dimension features are then used recursively as the main inputs to the RBF neural network. The developed online soft sensors are finally tested on a highly nonlinear distillation column benchmark problem to illustrate their effective performances. The simulation results demonstrate the superiority of the proposed soft sensor based on the combined recursive PCA and the RBF neural network.

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Section: A Rbf Neural Networkmentioning

confidence: 99%

Design of online soft sensors based on combined adaptive PCA and RBF neural networks

Salahshoor¹,

Kordestani²,

Khoshro³

2009

2009 IEEE Symposium on Computational Intelligence in Control and Automation

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“…Radial functions are a special class of functions, their characteristic feature is that response decreases, or increases, monotonically with distance from a center point [11,12]. It is a feed-forward three-layer network with single hidden layer [13]. It simulates the network structure of local readjustment in human brain, covering receptive field with each other.…”

Section: Rbf Neural Networkmentioning

confidence: 99%

Single Neuron PID Model Reference Adaptive Control Based on RBF Neural Network

Zhang

2006

2006 International Conference on Machine Learning and Cybernetics

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Radial Basis Function (RBF) neural network (NN) is powerful computational tools,which have been used extensively in the areas of pattern recognition, systems modeling and identification due to the advantages of simple construction, adaptability and robustness. This paper presents a novel approach of single neuron PID model reference adaptive control (MRAC) control based on RBF neural network on-line identification. A RBF network is built to identify the system on-line, and then it constructs the on-line reference model, implements self-learning of controller parameters by single neuron controller, and thus achieves on-line regulation of controller's parameters. The simulationresult shows that the proposed method can construct processing model through on-line identification and then give gradient information to neuron controller, it can achieve on-line identification and on-line control with high control accuracy and good dynamic performance.

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“…A RBF network is a three-layer feed-forward neural network [7,8]. The mapping from input to output is nonlinear, but from hidden layer to output layer is linear.…”

Section: Identification Rbf Neural Networkmentioning

confidence: 99%

Adaptive PID Speed Controller Based on RBF for Permanent Magnet Synchronous Motor System

Zhang

Song

et al. 2010

2010 International Conference on Intelligent Computation Technology and Automation

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An adaptive speed PID controller based on RBFNN is investigated for PMSM servo system in this paper. RBF is used to identify the plant first. Then the Jacobian information of controlled plant obtained by RBFNN identification is used to adjust the gain of speed PID controller on-time. A robust parameters self-tuning learning algorithm is adopted to train the ANN for fast convergence. Simulation result shows that the proposed controller has the adaptability, strong robustness and satisfactory control performance in PMSM servo system.

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An introduction to radial basis functions for system identification. A comparison with other neural network methods

Cited by 26 publications

References 19 publications

Design of online soft sensors based on combined adaptive PCA and RBF neural networks

Design of online soft sensors based on combined adaptive PCA and RBF neural networks

Single Neuron PID Model Reference Adaptive Control Based on RBF Neural Network

Adaptive PID Speed Controller Based on RBF for Permanent Magnet Synchronous Motor System

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