In order to shorten the fussy experimental process in heat treatment of 7003 aluminum alloy, back-propagation (BP) artificial neural network control of scheme has been proposed. The network of arithmetic has been dedu...
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ISBN:
(纸本)9783037850756
In order to shorten the fussy experimental process in heat treatment of 7003 aluminum alloy, back-propagation (BP) artificial neural network control of scheme has been proposed. The network of arithmetic has been deduced by using gradient descent algorithms A BP neural network has been established between the heat treatment technique and the hardness. The results indicated that the predicted results are closed to the test results. The weakness that the nonlinear and time variation relationship between heat treatment and the hardness could be approached more accurately, effectively by using single-factor-experiment method has been overcome. Hence providing a effective, economical,rapid way for the heat treatment optimization of nonferrous metals and ferrous metal.
In order to shorten the fussy experimental process in heat treatment of 7003 aluminum alloy, back-propagation(BP) artificial neural network control of scheme has been proposed. The network of arithmetic has been deduc...
详细信息
In order to shorten the fussy experimental process in heat treatment of 7003 aluminum alloy, back-propagation(BP) artificial neural network control of scheme has been proposed. The network of arithmetic has been deduced by using gradient descent algorithms. A BP neural network has been established between the heat treatment technique and the hardness. The results indicated that the predicted results are closed to the test results. The weakness that the nonlinear and time variation relationship between heat treatment and the hardness could be approached more accurately, effectively by using single-factor-experiment method has been overcome. Hence providing a effective, economical,rapid way for the heat treatment optimization of nonferrous metals and ferrous metal.
A novel stochastic gradient algorithm for finite impulse response (FIR) adaptive filters, termed the least sum of exponentials (LSE), is introduced. In order to provide a generalisation of the class of weighted mixed ...
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ISBN:
(纸本)9781424408818
A novel stochastic gradient algorithm for finite impulse response (FIR) adaptive filters, termed the least sum of exponentials (LSE), is introduced. In order to provide a generalisation of the class of weighted mixed norm algorithms and at the same time avoid problems associated with a large number of free parameters of such algorithms, LSE is derived by minimising a sum of error exponentials. A rigourous mathematical analysis is provided, resulting in closed form expressions for the optimal weights and the upper bound of the learning rate. The analysis is supported by simulations in a system identification setting.
This paper studies the problem of letting an autonomous mobile robot negotiate obstacles in an optimal manner. In particular, a multi-modal control problem is addressed, where different modes of operation control the ...
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This paper studies the problem of letting an autonomous mobile robot negotiate obstacles in an optimal manner. In particular, a multi-modal control problem is addressed, where different modes of operation control the robot at different locations in the state space. The specification of the optimal discrete event dynamics is pursued through the design of optimal, parametrized switching surfaces, using results on switching surface optimization.
A nonlinear gradientdescent (NGD) learning algorithm with an adaptive amplitude of the nonlinearity is derived for the class of nonlinear finite impulse response (FIR) adaptive filters (dynamical perceptron). This is...
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A nonlinear gradientdescent (NGD) learning algorithm with an adaptive amplitude of the nonlinearity is derived for the class of nonlinear finite impulse response (FIR) adaptive filters (dynamical perceptron). This is based on the adaptive amplitude backpropagation (AABP) algorithm for large-scale neural networks. The amplitude of the nonlinear activation function is made gradient adaptive to give the adaptive amplitude nonlinear gradientdescent (AANGD) algorithm, making the AANGD suitable for processing nonlinear and nonstationary input signals with a large dynamical range. Experimental results show the AANGD algorithm outperforming the standard NGD algorithm on both colored and nonlinear input with large dynamics. Despite its simplicity, the considered algorithm proves suitable for adaptive filtering of nonlinear and nonstationary signals.
A fully adaptive normalized nonlinear gradientdescent (FANNGD) algorithm for online adaptation of nonlinear neural filters is proposed. An adaptive stepsize that minimizes the instantaneous output error of the filter...
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A fully adaptive normalized nonlinear gradientdescent (FANNGD) algorithm for online adaptation of nonlinear neural filters is proposed. An adaptive stepsize that minimizes the instantaneous output error of the filter is derived using a linearization performed by a Taylor series expansion of the output error. For rigor, the remainder of the truncated Taylor series expansion within the expression for the adaptive learning rate is made adaptive and is updated using gradientdescent. The FANNGD algorithm is shown to converge faster than previously introduced algorithms of this kind.
Neural networks are receiving attention as controllers for many industrial applications. Although these networks eliminate the need for mathematical models, they require a lot of training to understand the model of a ...
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Neural networks are receiving attention as controllers for many industrial applications. Although these networks eliminate the need for mathematical models, they require a lot of training to understand the model of a plant or a process. Issues such as learning speed, stability, and weight convergence remain as areas of research and comparison of many training algorithms. This paper discusses the application of neural networks to control induction machines using direct torque control (DTC), A neural network is used to emulate the state selector of the DTC, The training algorithms used in this paper are the backpropagation, adaptive neuron model, extended Kalman filter, and the parallel recursive prediction error, Computer simulations of the motor and neural-network system using the four approaches are presented and compared, Discussions about the parallel recursive prediction error and the extended Kalman filter algorithms as the most promising training techniques is presented, giving their advantages and disadvantages.
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