Neurofuzzy control

Neurofuzzy eontrol eombines the mapping and learning ability of an artifieial neural network with the linguistie and fuzzy inferenee advantages of fuzzy logie. Thus 

The Adaptive Network based Fuzzy Inference System (ANFIS) 

Square nodes in the ANFIS structure denote parameter sets of the membership functions of the TSK fuzzy system. Circular nodes are static/non-modifiable and perform operations such as product or max/min calculations. A hybrid learning rule is used to accelerate parameter adaption. This uses sequential least squares in the forward pass to identify consequent parameters, and back-propagation in the backward pass to establish the premise parameters. 

Layer 1 contains adaptive nodes that require suitable premise membership functions (triangular, trapezoidal, bell, etc). Hence 

Layer 3 calculates the ratio of the firing strength of the rules 

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The ANFIS neurofuzzy controller was implemented by Jang (1993) and employs a Takagi-Sugeno-Kang (TSK) fuzzy inference system. The basic ANFIS architecture is shown in Figure 10.31. 

Application of adaptive neurofuzzy control using soft sensors to continuous distillation... 

Neural and fuzzy applications have been successfully applied to the chemical engineering processes [1], and several control strategies have been reported in literature for the distillation plant modeling and control tasks [2]. Recent years have seen a rapidly growing number of neurofuzzy control applications [3]. Beside this, several software products are currently available to help with neurofuzzy problems. 

Application of Adaptive Neurofuzzy Control Using Soft Sensors to Continuous Distillation... 

P. Rusu, E. M. Petriu, T. E. Whalen, A. Cornell H. J. W. Spoelder (2003), Behavior-based neurofuzzy controller for mobile robot navigation, IEEE Trans. Instrum. Measur. vol.52. No. 4, pp.1335-1340, (Aug, 2003). 

Brown, M. and Harris, C. (1994) Neurofuzzy Adaptive Modelling and Control, Prentice-Hall International (UK) Hemel Hempstead, UK. 

Keywords distillation control, neurofuzzy networks, soft sensors, genetic algorithms... 

Rouhani, H., Jalili, M, Araabi, B. N., Eppler, W., Lucas, C. (2006). Brain emotional learning based intelligent controller applied to neurofuzzy model of micro-heat exchanger. Expert Systems with Applications, 32, 911-918. doi 10.1016/j. eswa.2006.01.047... 

Brown M., Harris C., Neurofuzzy adaptive modelling and control, 1994, Prentice HaU International, UK. 

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