PSO-BELBIC scheme for two-coupled distillation column process

Dorrah, Hassen T.; El-Garhy, Ahmed M.; EL-Shimy, M.

doi:10.1016/j.jare.2010.08.004

Cited by 42 publications

(25 citation statements)

References 23 publications

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“…HassenT.Dorrah et al proposed PSO-BELBIC for two-coupled distillation column process. The PSO_BELBIC improves the time domain parameters of all loops of the process [7]. PSO and its variants widely are applied for tuning of PID controller parameters [8]- [12].…”

Section: Introductionmentioning

confidence: 99%

A Novel Particle Swarm Optimization with Passive Congregation via Chaotic Sequences

Gholipour¹,

Mojallali²,

Akhlaghi³

2012

IJCEE

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Abstract-This paper proposes a novel particle swarm optimization with passive congregation (PSOPC) algorithm based on the chaos phenomenon. The proposed Chaotic PSOPC (CPSOPC) algorithm utilizes chaotic sequences to improve the global searching by escaping the local optima. This algorithm overcomes the disadvantages of premature convergence in the PSOPC. We compared the proposed algorithm with PSOPC for eight benchmark functions with 10 and 20 dimensions. Simulation results indicate that the CPSOPC improves the global convergence of PSOPC. Furthermore, the proposed algorithm improves the accuracy and efficiency of searches compared with PSOPC and increases the convergence speed of PSOPC.

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Section: Introductionmentioning

confidence: 99%

A Novel Particle Swarm Optimization with Passive Congregation via Chaotic Sequences

Gholipour¹,

Mojallali²,

Akhlaghi³

2012

IJCEE

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“…PSO as evolutionary computation techniques shares many similarities with GA, but PSO offer easy implementation with few adjustable gains. PSO is considered as a fast-developing research topic and applied successfully in optimization function, artificial neural network training, and fuzzy system control [5]. The biological dispositions of animals and mimics bio mechanisms have inspired the BI system.…”

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confidence: 99%

“…The biological dispositions of animals and mimics bio mechanisms have inspired the BI system. Since 1990s, the NN technology has become one of most attractive topics for the scientific community, and growth rapidly in different and various applications [1], [2], [5].…”

mentioning

confidence: 99%

“…The orbitofrontal cortex receives inputs from the cortical areas and the amygdala and responsibles for the reaction to change the contingency of emotions. Error of the expected reward or punishment and the loss of learning in the amygdala will be managed by orbitofrontal cortex [1], [2], [5].…”

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confidence: 99%

“…During the past few years, the BELBIC has been successfully employed for making decisions and controlling in a simple linear systems and non-linear systems such as speed control of a permanent magnet synchronous motor (PMSM), automatic voltage regulator (AVR) system, flight control, position tracking and swing damping control of single input multi output overhead traveling crane, washing machine, automotive suspension control system, micro-heat exchanger, ventilating and air conditioning control [1], [2], [5]. In further development, BELBIC was implemented on field programmable gate array (FPGA) for controlling a mobile crane in a model free and embedded manner.…”

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confidence: 99%

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Progress in Artificial Intelligence Techniques: from Brain to Emotion

2011

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Artificial Intelligence (AI) techniques, e.g. expert system (ES), fuzzy logic (FL), artificial neural network (ANN), genetic algorithm (GA), particle swarm optimization (PSO) and biologically inspired (BI) have recently been applied widely in power electronics and motor drives. The aim of the AI is to model natural or human intelligence in a computer to think smartly like a human [1], [2].The next form of AI is the embedded AI controller system which has ability in learning, self-organizing, and self-adapting. Had been able to solve common and complex control problem, the AI technique in computational intelligence applied in wide application of industrial process control, robotics, automated planning and scheduling, games, hypermedia, image processing, patterns recognition (handwriting, speech, and facial), logistics, data mining, medicine and healthcare, space and diagnostic technology [1].Each AI method has its own uniqueness and characteristics. The ES and FL techniques tend to mimic the behavioural nature of the human brain and base on the rules; the NN is more generic in nature and tends to pattern directly to the biological NN. The GAs and the evolutionary computation techniques are based on principles of genetics. Basically, GA solves optimization problems through a searching process to find the fittest as a survivor for the best solutions. Among all the sub branches of AI, the NN and FL appear to be most uses for highperformance motor drives. However there are many other feed forward and recurrent NN topologies which require systematic exploration for their applications [3]. In advance, the powerful intelligent control and estimation techniques are dynamically developed through hybrid AI systems such as neuro-fuzzy, neuro-genetic, and neuro-fuzzy-genetic systems. The PSO as a population-based stochastic optimization technique has been developed since 1995 and inspired by social behavior of bird flocking or fish schooling [4]. PSO as evolutionary computation techniques shares many similarities with GA, but PSO offer easy implementation with few adjustable gains. PSO is considered as a fast-developing research topic and applied successfully in optimization function, artificial neural network training, and fuzzy system control [5]. The biological dispositions of animals and mimics bio mechanisms have inspired the BI system. Since 1990s, the NN technology has become one of most attractive topics for the scientific community, and growth rapidly in different and various applications . Basically, the brain emotional learning (BEL) is divided into two parts: amygdala and orbitofrontal cortex. The amygdala is a part of the brain that must be responsible for processing emotions primarly and correspond with the orbitofrontal cortex, thalamus, and sensory input cortex in the network model. The orbitofrontal cortex receives inputs from the cortical areas and the amygdala and responsibles for the reaction to change the contingency of emotions. Error of the expected reward or punishment and the loss of learning in the ...

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Online and stable parameter estimation based on normalized brain emotional learning model (NBELM)

Akhormeh

Roshanian

MoradiMaryamnegari

et al. 2019

Adaptive Control & Signal

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Recently, a computational model of Amygdala based on the brain emotional learning is presented by psychologists. This brain emotional learning model (BELM) that has a neuro-inspired architecture is utilized to train the weights which are in Amygdala and Orbitofrontal. In this paper, unknown parameters of dynamic systems are estimated by developing the normalized BELM (NBELM).To this end, after proving the stability of the model output, the sufficient condition for weights convergence is extracted while the sensitivity analysis is applied for this model. In order to evaluate the performance of NBELM, in the first example, the matrices of a twin rotor MIMO system are estimated and compared with the equation error method (EEM). In the second example, the nonlinear model of a servomotor is utilized as a case study. In the third example, the performance of the NBELM in experimental systems is validated using a reaction wheel with a DC motor. An important feature of the brain emotional system is its fast response, leading the NBELM to have a high speed performance in estimating the parameters of dynamic systems. A few number of adjustable parameters and low computing complexity also cause the NBELM to be an appropriate method for online estimation of the unknown parameters of dynamic systems. KEYWORDSAmygdala computing model, BEL model, dynamic system parameter estimation, online and stable estimation Parameters of a large number of industrial systems need to be monitored online for various reasons, ranging from adaptive controls 1 to fault detection systems. 2,3 As a consequence, many research studies have been devoted to introducing novel parameters identification algorithms able to insure the stability of the system. Additionally, the proposed techniques must converge to real values as fast as possible in order to be applicable to engineering systems while high accuracy in the converging process is an evitable need in any novel method. The low complexity of the new algorithm is another challenge, leading the system identification method to be implemented with less effort and become wider. For mentioned challenges to be addressed, various investigations create new frameworks in terms of problem formulation, methodology development, theoretical foundation, software tools, and hardware modules. 4Int J Adapt Control Signal Process. 2019;33:1047-1065.wileyonlinelibrary.com/journal/acs

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PSO-BELBIC scheme for two-coupled distillation column process

Cited by 42 publications

References 23 publications

A Novel Particle Swarm Optimization with Passive Congregation via Chaotic Sequences

A Novel Particle Swarm Optimization with Passive Congregation via Chaotic Sequences

Progress in Artificial Intelligence Techniques: from Brain to Emotion

Online and stable parameter estimation based on normalized brain emotional learning model (NBELM)

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