Download Full Journal - Pakistan Academy of Sciences

66 Arshad H. Malik and Feroza Arshadcontrol systems [3].Different types of neuro-fuzzy systems areused for different applications of identification,modeling, control, fault detection and expertsystems purposes. An Adaptive Network FuzzyInference System (ANFIS) neuro-fuzzy systemhas been reported in [1] for designing themaritime structures. In [3], an ANFIS-basedmodeling and feedforward control system hasbeen developed for shape memory alloyactuators. An ANFIS-based PID controller hasbeen developed for robot manipulator [4]. Aradial basic function neural network basedANFIS neuro-fuzzy controller has beenidentified for robotic manipulator application[5]. Another application of neuro-fuzzycontroller for the navigation of mobile robotusing artificial neural network based ontrigonometric series has been reported [6].Computational accuracy of ANFIS and ANN asnonlinear models and Auto-RegenerativeIntegrated Moving Average (ARIMA) as linearmodel for forecasting applications have beenanalyzed and presented [7].Neural networks have been extensivelyinvestigated in the context of systemidentification and simulation for nuclear researchreactor [8-10]. Recurrent type ANN has beenused for the prediction of flux and core power indifferent types of Pressurized Water Reactors(PWRs) [11, 12]. Ann attempt has been made fordesigning an intelligent Multi-Input-Multi-Output (MIMO) PWR core power controllerusing nuclear reactor code, recurrent type ANNand fuzzy system with fuzzifier and defuzzifier[13]. An improved intelligent MIMO PWRcontroller for axial off-set control usingrecurrent type ANN, fuzzy system withSingleton Fuzzifier, a Product Inference Engineand a Center of Gravity Defuzzifier (SF-PIE-CGD) has been reported [14]. A prediction ofChannel Power Distribution (CPD) for an IndianPHWR has been proposed [15]. Theidentification of nonlinear dynamics of PHWRhas been recently investigated using AdaptiveFeedforward Neural Network (AFNN) [16]; and,thus, a research effort has been put fordeveloping nonlinear models of different reactorsystem using feedforward neural networkoptimized by generalized delta rule withmomentum weight and bias learning rules. APPCS has been designed for Indian PHWR usingfuzzy logic controller [17]. This fuzzy controlleris a simple controller designed for feed andbleed system using seven triangular membershipfunctions only.In this research work, a new design approachis attempted for the successful replacement ofprimary pressure control system of a differentPHWR-type nuclear power plant. Our proposeddesign methodology is one step ahead fromresearch work reported [16, 17]. In this paper, anew MIMO hybrid intelligent adaptive neurofuzzysystem is designed based on theintegration of Adaptive Feedforward NeuralNetwork [16] and Mamdani-type FuzzyInference System (MFIS) in the frame work ofparallel learning of 24 each of triangular andtrapezoidal membership functions for inputs andforty five triangular and sixteen trapezoidalmembership functions for outputs. Therefore, aparallel learning and parallel data processing hasbeen adopted with clustering algorithm for theselection of membership functions, advancedintelligent soft computing and neuraloptimization for fuzzy rule reduction for adifferent PHWR-type nuclear power plant. TheMamdani adaptive neuro-fuzzy intelligentsystem is better than ANFIS in expression ofconsequent part and intuitive fuzzy reasoning.In this MOMO-HI system, an operator andimplication operator is product and aggregateoperator is sum and defuzzification operator iscentroid of area. Therefore, a compositeinference method is evolved which results inexcellent ability of learning because ofdifferentiability while soft intelligentcomputing. It resolves some difficulties thatare associated with other intelligent inferencesystems by providing multi-parametercomputational facility and easy weightassigning facility to each input and fuzzyrules. Thus, it has a great flexibility withmulti-parameter synthetic evaluation which isa main issue of T-S fuzzy inference system.The unique aspect of this control system is thehybrid intelligent critic which simulates anexpert’s operation in reality. Thischaracteristic increases the degree ofintelligence and robustness of the system andresults in self tuning and adaptive controllerdesign. This MIMO-HI has advantages innonlinear modeling, membership functions inconsequent parts, scale of training intelligentdata and amount of adjusted parameters. Thepower of proposed MIMO-HI system is it’sintuitive reasoning, widespread acceptanceand it’s well suited to human cognition whichare not achievable in ANFIS model.