neural network based approximation
Design of Hybrid Fuzzy Neural Network for Function Approximation
... The conventional approaches to system modeling that are based on mathematical tools (i.e. differential equations) perform poorly in dealing with complex and uncertain systems. The basic reason is that, most of the ...
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Continuous Approximation of Nonlinear L1 Problem Based on BP Neural Network
... there is a BP neural network which can approximate to g by an arbitrary accuracy. In other words, with any given set of training samples, assuming the set of input signals is X and the set of output ...
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ABSTRACT: Chaos and chaos control are new theories and new fields of nonlinear dynamics. Chaotic motion
... Artificial neural network has been proved to have the characteristics of arbitrary approximation to nonlinear ...artificial neural network with chaotic system, some scholars have ...
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Exploring optimal architecture of Multi layered Feed forward (MLFNN) as Bidirectional Associative Memory (BAM) for Function Approximation
... Function approximation is the task of learning or constructing a function that generates approximately the same outputs from input vectors as the process being modeled, based on available training ...
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Generation of Lyapunov Functions by Neural Networks
... of network and method of choosing learning ...conditions based on the amount of sensitivity of state trajectories in relation to them ...the network tries to mimic the negative definite ...a ...
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I. INTRODUCTION UNCTION approximation refers to finding an approximate
... Basis Neural Network (RBNN) genetic algorithms, Particle Swarm Optimization (PSO), and Self Organizing Map ...functions based on standalone models and their proposed hybrid ...
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A generalized procedure in designing recurrent neural network identification and control of time varying delayed nonlinear dynamic systems
... nonlinearities. Neural network based inductive techniques, subject to their inherent approximation capabilities, have been found to be very supportive for controlling such class of complex ...
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Verification of an Evolutionary-based Wavelet Neural Network Model for Nonlinear Function Approximation
... An important issue involved in system modeling is the identification of structure and function of a system. The aim of system identification is to identify a predefined simulation model that approximates a real world ...
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Designinga Neuro-Sliding Mode Controller for Networked Control Systems with Packet Dropout
... adaptive neural sliding mode control based on radial-basis function neural network approximation is proposed to eliminate chattering phenomenon in the sliding mode ...
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Teleoperation of SCARA with Neural Network Based Controller
... interface, based on the neural network control approach for a d a p ta tio n to en v iro n m e n t d istu rb a n c e s ...teaching neural network robot control techniques in ...the ...
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Application of kohonen neural network and rough approximation for overlapping clusters optimization
... The approach is based on the rough set theory that employs a soft clustering which can detects overlapped data from the data set and makes clustering as precise as possible,[r] ...
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Multi-Input Multi-Output Direct Adaptive Control for a Distributed Parameter Flexible Rotating Arm
... modeling, neural network approximation and energy-based approaches are used, in combination with adaptation mechanisms, to adjust neural network weights [33], to tune fuzzy ...
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PSO based neuro fuzzy sliding mode control for a robot manipulator
... SMC. Neural networks (NNs), Fuzzy logic and Neuro-fuzzy are combined with SMC and used in non-linear, time variant and uncertain ...A neural network based sliding mode adaptive control ...
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Quantum Inspired Neural Network with Sequence Input
... the approximation and generalization ability of ANN, we proposed a quantum-inspired neural network model with sequence input, called ...considering approximation ability and computational ...
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Forcing neurocontrollers to exploit sensory symmetry through hard-wired modularity in the game of Cellz
... In the convoluted architectures, each module gets the full range of sixteen inputs, but they are displaced according to the position of the module (e.g. module number 3 gets food inputs 3, 4, 5, 6, 7, 0, 1, 2, in that ...
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OPTIMIZATION OF CONSECUTIVE SIGNALIZED INTERSECTIONS BASED ON COMBINED ALGORITHMS – COMPARING RESULTS WITH MICROSIMULATION
... backward network, hidden layers can form main core of calculations and these layers can receive inputs from input layer using neurons and then, they produce amount of output in output ...whole network and ...
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Prediction of Tourist Quantity Based on RBF Neural Network
... Artificial neural network (ANN) modeling method is an effective analysis method for forecasting, which can well reveal the correlation of nonlinear time series in delay state ...in neural ...
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OPTIMIZATION OF HIGH VOLTAGE POWER SUPPLY FOR INDUSTRIAL MICROWAVE GENERATORS FOR ONE MAGNETRON
... RBF neural network and weight values from the hidden layer to the output ...optimized neural network was used for transformer fault ...methods based on conventional RBF neural ...
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A comparative performance evaluation of neural network based approach for sentiment classification of online reviews
... for neural network based text sentiment classifi- cation are found to be rare from the literature (Zhu et ...about neural network based ensemble methods contributing to sentiment ...
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Interpolation of Generalized Functions Using Artificial Neural Networks
... where * denotes the convolution operation. This function is called ramp func- tion and has many applications in engineering (it is used in the so-called half-wave rectification, which is used to convert alternating ...
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