Model-free PID control tuning methods have been widely reported for the case with the time-varying uncertain systems. A model-free control system is introduced in this paper with PID controller. This control design co...
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ISBN:
(纸本)9781479917631
Model-free PID control tuning methods have been widely reported for the case with the time-varying uncertain systems. A model-free control system is introduced in this paper with PID controller. This control design could be considered as a contribution to kernel wavelet neural network PID controllers in model-free system with a straightforward tuning by the simultaneous perturbation stochastic approximation algorithm. It is particularly well suited to problems involving the measurement data without prior inner structure knowledge of the unknown system. On the other hand, neural network activation functions are bounded which cause the outputs of neural network to be limited. Therefore, the T-S Fuzzy control method on basis of kernel wavelet neural network (KWNN) is introduced to improve the control parameter tuning adaptivity in terms of boundedness of the KWNN excitation function. Finally, simulation results are presented to exhibit the effectiveness of the proposed IPWR control system.
Introduction The paper deals with the adjustment of time-dependent Origin-destination (O-D) demand matrix, which is the fundamental input of ITS application for traffic predictions. The usual problem is to search for ...
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Introduction The paper deals with the adjustment of time-dependent Origin-destination (O-D) demand matrix, which is the fundamental input of ITS application for traffic predictions. The usual problem is to search for temporal O-D matrices that are "near" an a priori estimate (seed matrices) and that best fit traffic counts. However information on link flows is not fully effective in describing the state of the network;recent technologies for tracking vehicles provide a new kind of information on route travel times that can integrate usual information on traffic flows at count sections. Objective The object of the paper is to analyse the effectiveness of different types of information in the off-line simultaneous adjustment of dynamic O-D demand, starting from seed matrices with different degrees of reliability.
An increase in the performance of deteriorating systems can be achieved through the adoption of suitable maintenance policies. One of the most popular maintenance policies is the age-dependent replacement policy. In t...
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An increase in the performance of deteriorating systems can be achieved through the adoption of suitable maintenance policies. One of the most popular maintenance policies is the age-dependent replacement policy. In this paper, a fuzzy agedependent replacement policy is considered in which the lifetimes of components are treated as fuzzy variables. To minimize the long-term expected cost per unit time, a programming model is formulated. Also, a theorem for optimal solution existence is proposed. In order to solve the proposed model, a fuzzy simulation technique is designed which estimates the expected value of the objective function. The simultaneous perturbation stochastic approximation (spsa) algorithm is then used to determine a solution. Finally, a numerical example is presented to illustrate the effectiveness of this technique. (c) 2007 Published by Elsevier Inc.
This paper proposes a new type fuzzy neural systems, denotes IT2RFNS-A (interval type-2 recurrent fuzzy neural system with asymmetric membership function), for nonlinear systems control. To enhance the performance and...
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ISBN:
(纸本)9781424473175
This paper proposes a new type fuzzy neural systems, denotes IT2RFNS-A (interval type-2 recurrent fuzzy neural system with asymmetric membership function), for nonlinear systems control. To enhance the performance and approximation ability, the TSK-type consequent part is adopted for IT2RFNS-A. The gradient information of the IT2RFNS-A is not easy to obtain due to the asymmetric membership functions and interval valued sets. The corresponding stable learning is derived by simultaneous perturbation stochastic approximation (spsa) algorithm which guarantees the convergence and stability of the closed-loop systems. Simulation and comparison on the control of Chua's chaotic circuit is done to show the feasibility and effectiveness of proposed method.
A sudden traffic surge immediately after special events (e.g., conventions, hockey games, concerts, etc.) can create substantial traffic congestion in the area where the events are held. It is desired to implement a s...
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ISBN:
(纸本)9789806560543
A sudden traffic surge immediately after special events (e.g., conventions, hockey games, concerts, etc.) can create substantial traffic congestion in the area where the events are held. It is desired to implement a short-term traffic signal timing adjustment for the high volume traffic movements associated with special events so that progression is as efficient as possible. This paper presents a case study of special events traffic signal timing control for die City of Duluth Entertainment and Convention Center (DECC). Our optimization approach is based on neural networks (NNs) with the weight estimation via the Simultaneous Perturbation Stochastic Approximation (spsa) algorithm. Using the traffic data collected, the NN-based spsa optimization method is applied to make signal timing adjustments. A tolerance index is chosen as our measure-of-effectiveness;(MOE). The NN weights are determined by use of the spsa parallel estimation algorithm that minimizes the MOE criterion at the selected intersections following DECC events. The performance evaluations, based on different MOEs, using the existing signal timing and the one generated by the spsa algorithm are investigated. The results show the potential of the proposed optimization method.
Practitioners of iterative optimization techniques want their chosen algorithm to reach the global optimum rather than get stranded at a local optimum value. In this article, we discuss two theorems on the global conv...
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Practitioners of iterative optimization techniques want their chosen algorithm to reach the global optimum rather than get stranded at a local optimum value. In this article, we discuss two theorems on the global convergence of an algorithm called Simultaneous Perturbation Stochastic Approximation (spsa) that has performed well in complex optimization problems. The first provides conditions under which spsa will converge in probability to a global optimum using the well-known method of injected noise. In the second theorem we show that, under different conditions, "basic" spsa without injected noise can achieve convergence in probability to a global optimum. This global convergence without injected noise can have important benefits in the setup and performance of the algorithm. We present a numerical study comparing the global convergence of spsa to that of a genetic algorithm.
The authors propose a two-timescale version of the one-simulation smoothed functional (SF) algorithm with extra averaging. They also propose the use of a chaotic simple deterministic iterative sequence for generating ...
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The authors propose a two-timescale version of the one-simulation smoothed functional (SF) algorithm with extra averaging. They also propose the use of a chaotic simple deterministic iterative sequence for generating random samples for averaging. This sequence is used for generating the N independent and identically distributed (i.i.d.), Gaussian random variables in the SF algorithm. The convergence analysis of the algorithms is also briefly presented. The authors show numerical experiments on the chaotic sequence and compare performance with a good pseudo-random generator. Next they show experiments in two different settings-a network of M/G/1 queues with feedback and the problem of finding a closed-loop optimal policy (within a prespecified class) in the available bit rate (ABR) service in asynchronous transfer mode (ATM) networks, using all the algorithms. The authors observe that algorithms that use the chaotic sequence show better performance in most cases than those that use the pseudo-random generator.
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