Search

The most useful property of neural networks for system identification and control is their ability to approximate arbitrary nonlinear mappings. In this paper, a new neural network model-following control method is proposed for nonlinear servo systems. First, three-layer artificial neural net works are trained to describe the forward and in verse dynamic characteristics of one type of speci fied nonlinear servo systems by using the backpropagation algorithm. Then the neural net work inverse model is used as a feedforward con troller for model-following control of the nonlin ear servo systems. Computer simulation results obtained from MATLAB verify the applicability of neural network identification and control for nonlinear servo systems. [ABSTRACT FROM PUBLISHER]

This paper proposes a wavelet-based fuzzy neuro control algorithm for the hazard mitigation of seismically excited buildings equipped with a hybrid control system. The wavelet-based fuzzy neuro controller is developed through the integration of discrete wavelet transform, artificial neural network, and a Takagi-Sugeno fuzzy controller. The hybrid control system is an integrated model of an actuator, a tuned mass damper, and viscous liquid dampers: an active tuned mass damper (ATMD) is located on the top floor of the structure and viscous liquid dampers are located on each floor. To demonstrate the effectiveness of the proposed wavelet-based adaptive neuro-fuzzy inference system (WANFIS) controller, an eight-story building employing passive viscous liquid dampers as well as an ATMD is investigated. A variety of earthquakes such as an artificial earthquake, the 1940 El-Centro, Kobe, Northridge, and Hachinohe earthquakes are used as disturbance signals. It is demonstrated that the WANFIS controller is effective in reducing the structural responses of the hybrid structure system subjected to a variety of disturbances. [ABSTRACT FROM AUTHOR]

The objective of this paper is to review some important progress with H infinity control methods and to derive an artificial algorithm for dealing with stability problems of multiple time-delay structural systems. Neural network modeling and linear deferential inclusion are utilized to represent a multiple time-delay structural system. We can derive a novel stability criterion using linear matrix inequality methods. Moreover, the H∞ control performance is ensured for multiple time-delay structural systems based on traditional Lyapunov theory. [ABSTRACT FROM PUBLISHER]

Quad tilt-rotor(QTR) UAV is a nonlinear time-varying system in full flight mode. It is difficult and inaccurate to model the nonlinear time-varying system, which cannot fully reflect the problem of controlling input and system response output in the full flight mode. In order to solve the above problems, a novel neural network model was adopt to identify the nonlinear time-varying system of quad tilt-rotor in full flight mode. An adaptive learning rate algorithm based on foraging strategy is proposed based on the global error BP neural network. Corresponding to the nonlinear time-varying system, BP neural network is set as the time-invariant system structure with constant network structure and continuously changing weights at multiple times, and the nonlinear input-output relationship under the time-varying system is jointly described by fitting the network at all times. The extended Kalman filtering algorithm is used to track the network connection weights by modifying the network weights at the current moment with the input and output data at the next moment. The final identification result shows that the smaller mean square error of both only transition process and full flight mode, shows that using this optimization algorithm can well describe the input and output characteristics of the nonlinear time-varying systems. When the same network structure is adopted, no matter for transition mode or full mode, the BP optimization algorithm based on foraging strategy is better than the global BP algorithm for system identification of the full mode quad tilt-rotor. Therefore, when the BP neural network based on foraging strategy is adopted, the same network structure can be adopted to systematically identify the full mode of quad tilt-rotor by changing the weight. [ABSTRACT FROM AUTHOR]

Manufacturing lead time estimation is an important task in production system with machine breakdown and maintenance. This study presents a flexible algorithm for estimation and forecasting lead time based on artificial neural network (ANN), fuzzy regression (FR), and conventional regression (CR). First, an ANN is illustrated based on supervised multi-layer perceptron network for the lead time forecasting. The selected ANN model is then compared with fuzzy and conventional regression models with respect to Mean Absolute Percentage Error, hence the name neuro-fuzzy regression algorithm. To show the applicability and superiority of the flexible neuro-fuzzy regression, the proposed algorithm is used to estimate the weekly lead times of an actual assembly shop (producer of heavy electric motor). This is the first study that introduces a flexible neuro-fuzzy algorithm for improved estimation of lead time in manufacturing systems with machine breakdown and maintenance. In addition to accuracy, simplicity and short execution time of lead time estimation are desirable features of the presented flexible algorithm. [ABSTRACT FROM PUBLISHER]

The purpose of this study was to develop a system to utilize the successful experiences and help the beginners of garment pattern design (GPD) through optimization methods. Size design of fit garments is a basic and difficult problem in GPD for inexperienced designers. We have proposed a hybrid system (NN-ICEA) based on neural network (NN) and immune co-evolutionary algorithm (ICEA) to predict the fit of the garments and search optimal sizes. ICEA takes NN as fitness function and procedures including clonal proliferation, hypermutation and co-evolution search the optimal size values. A series of experiments with a dataset of 450 pieces of pants was conducted to demonstrate the prediction and optimization capabilities of NN-ICEA. In the comparative studies, NN-ICEA was compared with NN-genetic algorithm to show the value of immune-inspired operators. Four types of GPD method have been summarized and compared. Moreover, the hybrid system for general features of garment has been discussed. The research is a feasible and effective attempt aiming at a valuable problem and provides key algorithms for fit prediction and size optimization. The algorithms can be incorporated into garment computer-aided design system. [ABSTRACT FROM AUTHOR]

Back-propagation (BP) neural network is used to develop process models for the parison extrusion in extrusion blow molding based on experimental data. In applying the BP network, some modifications, such as using a self-adaptive learning rate coefficient, determining the number of hidden neurons through experimentation, and so on, to the original BP algorithm are carried out to speed up learning. Quite a good agreement has been reached between the predicted parison length and swells using the trained BP models and the experimentally determined ones. The prediction of the parison diameter and thickness distributions can be made online at any parison length or any parison drop time within a given range using the trained models. It has been demonstrated that nonlinear swells, under the effect of sag, can be predicted within a reasonably adequate accuracy. [ABSTRACT FROM AUTHOR]