Direct Adaptive Soft Computing Neural Control of a Continuous Bioprocess via Second Order Learning

Centralized Distributed Parameter Bioprocess Identification and I-Term Control Using Recurrent Neural Network Model

“…The stability of the RTNN model is assured by the activation functions (-1, 1) bounds and by the local stability weight bound condition, given by (19). Below it is given a theorem of RTNN stability which represented an extended version of Nava's theorem, (Baruch et al, 2008;Baruch & MariacaGaspar, 2009;Baruch & Mariaca-Gaspar, 2010).…”

“…The general recursive L-M algorithm of learning, (Baruch & Mariaca-Gaspar, 2009;Baruch & Mariaca-Gaspar, 2010) is given by the following equations: …”

“…The RTNN topology is controllable and observable, and the BP/L-M algorithms of learning are convergent, (see Baruch & Mariaca-Gaspar, 2009;Baruch & Mariaca-Gaspar, 2010). Then the identification and control errors tend to zero (…”

“…The multilayer feedforward NN realizing a NARMA model for systems identification has the inconvenience that it is sequential in nature and require input and feedback tap-delays for its realization. In (Baruch et al, 2002;Baruch et al, 2004;Baruch et al, 2005a;Baruch et al, 2005b;Baruch et al, 2007a;Baruch et al, 2007b;Baruch et al, 2008;Baruch & Mariaca-Gaspar, 2009;Baruch & Mariaca-Gaspar, 2010), a new completelly parallel canonical Recurrent Trainable NN (RTNN) architecture, and a dynamic BP learning algorithm has been applied for systems identification and control of nonlinear plants with equal input/output dimensions, obtaining good results. The RTNN do not need the use of tap delays and has a minimum number of weights due to its Jordan canonical structure.…”