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1.
A scheme of adaptive control based on a recurrent neural network with a neural network compensation is presented for a class of nonlinear systems with a nonlinear prefix. The recurrent neural network is used to identify the unknown nonlinear part and compensate the difference between the real output and the identified model output. The identified model of the controlled object consists of a linear model and the neural network. The generalized minimum variance control method is used to identify parameters, which can deal with the problem of adaptive control of systems with unknown nonlinear part, which can not be controlled by traditional methods.Simulation results show that this algorithm has higher precision, faster convergent speed.  相似文献   
2.
张小平 《电讯技术》2000,40(5):72-75
介绍了一种新颖的非线性逢适应滤波器--循环神经网络自适应滤波器。由于这种循环神经等效于非线性IIR滤波器,具有学习非线性函数到任意的精度及自适应能力,这种滤波器优于线性滤波器,能够适应各种噪声环境。本文将该滤波器用于有源噪声对消,仿真结果表明了这种循环神经网络自适应有源噪声对消系统具有良好的抗噪声性能。  相似文献   
3.
Our objective is spoken-language classification for helpdesk call routing using a scanning understanding and intelligent-system techniques. In particular, we examine simple recurrent networks, support-vector machines and finite-state transducers for their potential in this spoken-language-classification task and we describe an approach to classification of recorded operator-assistance telephone utterances. The main contribution of the paper is a comparison of a variety of techniques in the domain of call routing. Support-vector machines and transducers are shown to have some potential for spoken-language classification, but the performance of the neural networks indicates that a simple recurrent network performs best for helpdesk call routing. Sheila Garfield received a BSc (Hons) in computing from the University of Sunderland in 2000 where, as part of her programme of study, she completed a project associated with aphasic language processing. She received her PhD from the same university, in 2004, for a programme of work connected with hybrid intelligent systems and spoken-language processing. In her PhD thesis, she collaborated with British Telecom and suggested a novel hybrid system for call routing. Her research interests are natural language processing, hybrid systems, intelligent systems. Stefan Wermter holds the Chair in Intelligent Systems and is leading the Intelligent Systems Division at the University of Sunderland, UK. His research interests are intelligent systems, neural networks, cognitive neuroscience, hybrid systems, language processing and learning robots. He has a diploma from the University of Dortmund, Germany, an MSc from the University of Massachusetts, USA, and a PhD in habilitation from the University of Hamburg, Germany, all in Computer Science. He was a Research Scientist at Berkeley, CA, before joining the University of Sunderland. Professor Wermter has written edited, or contributed to 8 books and published about 80 articles on this research area.  相似文献   
4.
A type of topological approach to mobile robot navigation is discussed and experimentally evaluated. The environment as experienced by a moving robot is treated as a dynamical system. Simple types of reactive behavior are supplemented with eventual decisions to switch between them. When switching criteria are defined, the system may be described in the form similar to a finite state machine. Since it is embedded in the environment and dependent on the sensory flow of the robot, we introduce the term “Embedded flow state machine” (EFSM). We implemented it with a recurrent neural network, trained on a sequence of sensory contents and actions. One of the main virtues of this approach is that no explicit localization is required, since the recurrent neural network holds the state implicitly. The EFSM is applicable to multi-step prediction of sensory information and the travelled distances between decision points, given a sequence of decisions at decision points. Thus, the optimal path to a specified goal can be sought. One of the main issues is, for how many steps ahead the prediction is reliable enough. In other words, is it feasible to perform environment modelling and path planning in this manner? The approach is tested on a miniature mobile robot, equipped with proximity sensors and a color video camera. Decision ‘points,’ where deviations from the wall-following behavior are allowed, are based on color object recognition. In the case of an experimental environment of medium complexity, this approach was successful.  相似文献   
5.
A novel neural network model is described that implements context-dependent learning of complex sequences. The model utilises leaky integrate-and-fire neurons to extract timing information from its input and modifies its weights using a learning rule with synaptic noise. Learning and recall phases are seamlessly integrated so that the network can gradually shift from learning to predicting its input. Experimental results using data from the real-world problem domain demonstrate that the use of context has three important benefits: (a) it prevents catastrophic interference during learning of multiple overlapping sequences, (b) it enables the completion of sequences from missing or noisy patterns, and (c) it provides a mechanism to selectively explore the space of learned sequences during free recall.  相似文献   
6.
We generalize a class of neural network models that extend the Kohonen Self-Organising Map (SOM) algorithm into the sequential and temporal domain using recurrent connections. Behaviour of the class of Activation-based Recursive Self-Organising Maps (ARSOM) is discussed with respect to the choice of transfer function and parameter settings. By comparing performances to existing benchmarks we demonstrate the robustness and systematicity of the ARSOM models, thus opening the door to practical applications.  相似文献   
7.
This research is concerned with a gradient descent training algorithm for a target network that makes use of a helper feed-forward network (FFN) to represent the cost function required for training the target network. A helper FFN is trained because the cost relation for the target is not differentiable. The transfer function of the trained helper FFN provides a differentiable cost function of the parameter vector for the target network allowing gradient search methods for finding the optimum values of the parameters. The method is applied to the training of discrete recurrent networks (DRNNs) that are used as a tool for classification of temporal sequences of characters from some alphabet and identification of a finite state machine (FSM) that may have produced all the sequences. Classification of sequences that are input to the DRNN is based on the terminal state of the network after the last element in the input sequence has been processed. If the DRNN is to be used for classifying sequences the terminal states for class 0 sequences must be distinct from the terminal states for class 1 sequences. The cost value to be used in training must therefore be a function of this disjointedness and no more. The outcome of this is a cost relationship that is not continuous but discrete and therefore derivative free methods have to be used or alternatively the method suggested in this paper. In the latter case the transform function of the helper FFN that is trained using the cost function is a differentiable function that can be used in the training of the DRNN using gradient descent.Acknowledgement. This work was supported by a discovery grant from the Government of Canada. The comments made by the reviewers are also greatly appreciated and have proven to be quite useful.  相似文献   
8.
This paper describes an adaptive neural control system for governing the movements of a robotic wheelchair. It presents a new model of recurrent neural network based on a RBF architecture and combining in its architecture local recurrence and synaptic connections with FIR filters. This model is used in two different control architectures to command the movements of a robotic wheelchair. The training equations and the stability conditions of the control system are obtained. Practical tests show that the results achieved using the proposed method are better than those obtained using PID controllers or other recurrent neural networks models  相似文献   
9.
Forecasting a hydrologic time series has been one of the most complicated tasks owing to the wide range of data, the uncertainties in the parameters influencing the time series and also due to the non availability of adequate data. Recently, Artificial Neural Networks (ANNs) have become quite popular in time series forecasting in various fields. This paper demonstrates the use of ANNs to forecast monthly river flows. Two different networks, namely the feed forward network and the recurrent neural network, have been chosen. The feed forward network is trained using the conventional back propagation algorithm with many improvements and the recurrent neural network is trained using the method of ordered partial derivatives. The selection of architecture and the training procedure for both the networks are presented. The selected ANN models were used to train and forecast the monthly flows of a river in India, with a catchment area of 5189 km2 up to the gauging site. The trained networks are used for both single step ahead and multiple step ahead forecasting. A comparative study of both networks indicates that the recurrent neural networks performed better than the feed forward networks. In addition, the size of the architecture and the training time required were less for the recurrent neural networks. The recurrent neural network gave better results for both single step ahead and multiple step ahead forecasting. Hence recurrent neural networks are recommended as a tool for river flow forecasting.  相似文献   
10.
We present in this paper a general model of recurrent networks of spiking neurons, composed of several populations, and whose interaction pattern is set with a random draw. We use for simplicity discrete time neuron updating, and the emitted spikes are transmitted through randomly delayed lines. In excitatory-inhibitory networks, we show that inhomogeneous delays may favour synchronization provided that the inhibitory delays distribution is significantly stronger than the excitatory one. In that case, slow waves of synchronous activity appear (this synchronous activity is stronger in inhibitory population). This synchrony allows for a fast ada ptivity of the network to various input stimuli. In networks observing the constraint of short range excitation and long range inhibition, we show that under some parameter settings, this model displays properties of –1– dynamic retention –2– input normalization –3– target tracking. Those properties are of interest for modelling biological topologically organized structures, and for robotic applications taking place in noisy environments where targets vary in size, speed and duration. This revised version was published online in June 2006 with corrections to the Cover Date.  相似文献   
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