Direct and Indirect Neural Identification and Control of a Continuous Bioprocess via Marquardt Learning

Baruch, Ieroham S.; Mariaca-Gaspar, Carlos-Roman; Barrera-Cortés, Josefina; Castillo, Oscar

doi:10.1007/978-3-642-15534-5_6

Cited by 4 publications

(6 citation statements)

References 14 publications

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“…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 & Mariaca-Gaspar, 2009;Baruch & Mariaca-Gaspar, 2010).…”

Section: Rtnn Topology and Recursive Bp Learningmentioning

confidence: 99%

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

Section: Recursive Levenberg-marquardt Rtnn Learningmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

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Recurrent Neural Networks and Soft Computing

ElHefnawi¹,

Mysara²

2012

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ITI) bioinformatics track. Many publications on topics including rational drug design, docking, and optimizing gene therapy for Influenza, Hepatitis C Virus (HCV), and different cancers, structural bioinformatics and motif predictions applications in virology, and applications of phylogenetic analysis. These were in reputed journals like Bioinformatics (Oxford University Press), PLOS1 (Public library of science), journal of molecular recognition, European journal of medicinal chemistry, and Virology Journal.Mr Mohamed Mysara, MSc graduate from the University of Nottingham, Dec/2010, acquiring the ITI prize for the best dissertation. Mr Mysara is working in the X Contents

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Section: Rtnn Topology and Recursive Bp Learningmentioning

confidence: 99%

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

Section: Recursive Levenberg-marquardt Rtnn Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Recurrent Neural Networks and Soft Computing

ElHefnawi¹,

Mysara²

2012

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“…Following their publication a large volume of published works for the last two and half decades by several researchers in the use of neural network in system identification have further reinforced the research activities with wider applications and greater practical importance. In the area of system identification, the research initiatives have been more concentrated on the application of NN for system identification mostly modeled with shift operator [2,3]. In recent years, the delta operator has been widely used to many areas in system and control.…”

Section: Introductionmentioning

confidence: 99%

Identification of Delta Operator System Using Neural Network

Ghosh

Sarkar²

2014

J. Inst. Eng. India Ser. B

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This paper proposes application of neural network in identification of dynamical system modelled with delta operator. The advantage of using delta operator such as greater numerical robustness in computation compared to shift operator is considered. Model for identification is implemented into realizable neural network structure using the inverse delta operator. Simulation example is presented to demonstrate that the proposed identification scheme works very well.

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“…Neural networks (NNs) have received considerable attention due to their extensive applications in various signal-processing problems such as optimization, fixed-point computations, and other areas in recent decades. Since integration and communication delays are unavoidably encountered both in biological and artificial neural systems, and may result in oscillation and instability, increasing interest has been focused on stability analysis of NNs with time delays [1–7, 9–20, 34–42].…”

Section: Introductionmentioning

confidence: 99%

Delay-range-dependent Stability Criteria of Neural Networks with Time-varying Discrete and Distributed Delays

Song

Zhang

et al. 2014

International Journal of Advanced Robotic Systems

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This article deals with the global asymptotic stability problem for a class of neural networks with timevarying discrete and distributed delays. The activation functions are assumed to be neither monotonic nor differentiable, and two types of time-varying discrete delays are considered: one is differentiable and has bounded derivatives, and the other is continuous and may vary very fast. By constructing an appropriate Lyapunov-Krasovskii functional and employing a tighter inequality, new stability criteria dependent on both the lower bound and upper bound of the time-varying time delays are established to guarantee asymptotic stability for the addressed neural networks. It is shown that the new criteria improve some results from previous studies. Two simulation examples are given to show the effectiveness and the reduced conservatism of the proposed criteria.

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Direct and Indirect Neural Identification and Control of a Continuous Bioprocess via Marquardt Learning

Cited by 4 publications

References 14 publications

Recurrent Neural Networks and Soft Computing

Recurrent Neural Networks and Soft Computing

Identification of Delta Operator System Using Neural Network

Delay-range-dependent Stability Criteria of Neural Networks with Time-varying Discrete and Distributed Delays

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