The search for an adequate strategy for on-line Tool Condition Monitoring (TCM) in an automated manufacturing environment is a distant goal, yet to be achieved. The present impetus is towards the application of neural...
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The search for an adequate strategy for on-line Tool Condition Monitoring (TCM) in an automated manufacturing environment is a distant goal, yet to be achieved. The present impetus is towards the application of neural networks with different learning schemes through the use of computers for faster processing. In this paper, the performance of the back-propagation neural network has been studied for various parameters. Moreover, the efficacy of a modified back-propagation algorithm for faster convergence has been evaluated for its applicability with a set of data on TCM, where reduction in computation time is very important. The results of the modified algorithm are quite encouraging for future applications in the area of on-line TCM. (C) 2000 Published by Elsevier Science S.A. All rights reserved.
This article presents the hardware implementation of the floating-point processor (FPP) to develop the radial basis function (RBF) neural network for the general purpose of pattern recognition and nonlinear control. T...
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This article presents the hardware implementation of the floating-point processor (FPP) to develop the radial basis function (RBF) neural network for the general purpose of pattern recognition and nonlinear control. The floating-point processor is designed on a field programmable gate array (FPGA) chip to execute nonlinear functions required in the parallel calculation of the back-propagation algorithm. Internal weights of the RBF network are updated by the online learning back-propagation algorithm. The on-line learning process of the RBF chip is compared numerically with the results of the RBF neural network learning process written in the MATLAB program. The performance of the designed RBF neural chip is tested for the real-time pattern classification of the XOR logic. Performances are evaluated by comparing results from the MATLAB through extensive experimental studies. (C) 2014 Elsevier By. All rights reserved.
In the present study, a new algorithm is developed for neural network training by combining a gradient-based and a meta-heuristic algorithm. The new algorithm benefits from simultaneous local and global search, elimin...
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In the present study, a new algorithm is developed for neural network training by combining a gradient-based and a meta-heuristic algorithm. The new algorithm benefits from simultaneous local and global search, eliminating the problem of getting stuck in local optimum. For this purpose, first the global search ability of the grey wolf optimizer (GWO) is improved with the Levy flight, a random walk in which the jump size follows the Levy distribution, which results in a more efficient global search in the search space thanks to the long jumps. Then, this improved algorithm is combined with backpropagation (BP) to use the advantages of enhanced global search ability of GWO and local search ability of BP algorithm in training neural network. The performance of the proposed algorithm has been evaluated by comparing it against a number of well-known meta-heuristic algorithms using twelve classification and function-approximation datasets.
In this paper, the convergence of a new back-propagation algorithm with adaptive momentum is analyzed when it is used for training feedforward neural networks with a hidden layer. A convergence theorem is presented an...
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In this paper, the convergence of a new back-propagation algorithm with adaptive momentum is analyzed when it is used for training feedforward neural networks with a hidden layer. A convergence theorem is presented and sufficient conditions are offered to guarantee both weak and strong convergence result. Compared with existing results, our convergence result is of deterministic nature and we do not require the error function to be quadratic or uniformly convex. (C) 2010 Elsevier B.V. All rights reserved.
As one of the most important displacements in producing petroleum, CO2 flooding has been developed for nearly 50 years around the world in order to improve the tertiary recovery. The recovery efficiency, R, is a key p...
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ISBN:
(纸本)9783037856499
As one of the most important displacements in producing petroleum, CO2 flooding has been developed for nearly 50 years around the world in order to improve the tertiary recovery. The recovery efficiency, R, is a key parameter in the CO2 displacement of crude oil. Traditionally, R is determined by conducting CO2 flooding experiment, requiring considerable resources and long time periods, with the consequence of a limited number of core plug evaluations for a particular reservoir. Thus, the estimation of R with mathematical models is developed in recent years, which also needs plenty of relevant parameters considered. The study reported in this paper uses artificial neural network to determine R. Five dimensionless variables are considered to analyse the CO2 immiscible displacement process. An optimal model is chosen with its suitable hidden layer nodes and activation functions for the hidden and output layers. Its performance is compared with the numerical simulation model, demonstrating the superior performance of the proposed R prediction model.
This article presents the evaluation analysis of the radial function neural network embedded on an FPGA chip by experiments. The back-propagation algorithm has been embedded and tested for the feasibility for on-line ...
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ISBN:
(纸本)9781424421701
This article presents the evaluation analysis of the radial function neural network embedded on an FPGA chip by experiments. The back-propagation algorithm has been embedded and tested for the feasibility for on-line learning tasks. The nonlinear pattern classification task of the XOR logic has been conducted by the designed hardware. Performances are evaluated extensively by different orders of the Taylor-Maclaurin series expansion for approximating nonlinear functions and compared with results by the MATLAB program. The effects on the performance by the nonlinear function approximation have been analyzed by experimental studies of the XOR classification task.
This article presents the hardware implementation of the Radial Basis Function (RBF) neural network whose internal weights are updated in the real-time fashion by the back-propagation algorithm. The floating-point pro...
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ISBN:
(纸本)9781424418206
This article presents the hardware implementation of the Radial Basis Function (RBF) neural network whose internal weights are updated in the real-time fashion by the back-propagation algorithm. The floating-point processor is designed on a field programmable gat array (FPGA) chip to execute nonlinear functions required in the parallel processing calculation of the back-propagation algorithm. The performance of the on-line learning process of the RBF chip is compared numerically with the results of the RBF neural network learning program written in the MATLAB software under the same condition to check the feasibility of the implemented neural chip. The performance of the designed RBF neural chip is tested for the real-time pattern classification of the nonlinear XOR logic.
As one of the most important reservoir parameters, irreducible water saturation, S-wir, is a key parameter in evaluating multi-phase flow, as well as its importance in defining oil in-place. Residual oil saturation, t...
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ISBN:
(纸本)9783037852682
As one of the most important reservoir parameters, irreducible water saturation, S-wir, is a key parameter in evaluating multi-phase flow, as well as its importance in defining oil in-place. Residual oil saturation, the target of tertiary recovery, is also a function of S-wir. Traditionally, S-wir is determined by conducting capillary pressure experiments, requiring considerable resources and long time periods, with the consequence of a limited number of core plug evaluations for a particular reservoir. Thus, the estimation of S-wir with mathematical models is developed in recent years. The study reported in this paper uses artificial neural network to determine S-wir. The optimal model is chosen among 25 simulations, subtilizing different combinations of hidden layer nodes and activation functions for the hidden and output layers. Its performance is compared with other conventional models, demonstrating the superior performance of the proposed S-wir prediction models.
General Purpose Processors (GPPs) and ASICs have traditionally been the common means for building and implementing Artificial Neural Network's (ANNs). However Such computing paradigms suffer from the constant need...
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ISBN:
(纸本)0769524567
General Purpose Processors (GPPs) and ASICs have traditionally been the common means for building and implementing Artificial Neural Network's (ANNs). However Such computing paradigms suffer from the constant need of establishing a trade-off between flexibility and performance Due to the technological advance in the development of progammable logic devices, Field Programmable Gate Arrays (FPGAs) have become attractive for realizing ANNs. FPGAs have shown to exhibit excellent flexibility in terms of reprogramming the same hardware and at the same time achieving good performance by enabling parallel computation. In this paper various implementations of ANNs on FPGAs are investigated and compared. The research described in this paper proposes three partially parallel architectures and a fully parallel architecture to realize the back- propagationalgorithm on an FPGA. The proposed designs are coded in Handel-C and functionally, verified by synthesizing them on a Virtex2000e FPGA chip. The partially parallel architectures and the fully parallel architecture are found to be 2.25 and 4 times faster than the software implementation rcspectively for different benchmarks.
Fuzzy modeling is discussed in many literatures and there are numerous algorithms are proposed. back-propagation algorithm is an efficient algorithm for fuzzy modeling and many papers proposed the usage of such method...
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ISBN:
(纸本)9783037850190
Fuzzy modeling is discussed in many literatures and there are numerous algorithms are proposed. back-propagation algorithm is an efficient algorithm for fuzzy modeling and many papers proposed the usage of such method. But there exists potential risk of dead zone, abrupt inference surface and decreasing sensitivity for normal back-propagation algorithm in fuzzy modeling. This paper analysis the potential problems of normal algorithm and suggest a reformative back-propagation algorithm for fuzzy modeling. A complete algorithm is presented in the paper and some simulate result is discussed
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