A primary proposal for the application of FuzzyArt Map to process control and a comparison using a classic PI controller have been carried out. The results obtained are clearly superior as far as the speed and exactne...
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A primary proposal for the application of FuzzyArt Map to process control and a comparison using a classic PI controller have been carried out. The results obtained are clearly superior as far as the speed and exactness of the time response are concerned, showing a stability improvement in critical situations and ample tolerance when faced with noisy estimations. For this reasons, the neural net can be used in control applications, acting as identification, estimation and control blocks.
The paper deals with new developments on interpolating memories as the basic element of learning control and their possible applications. The paper addresses only three possible applications of learning: building a no...
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The paper deals with new developments on interpolating memories as the basic element of learning control and their possible applications. The paper addresses only three possible applications of learning: building a nonlinear process model to investigate the effects of possible control strategies;automatic formation of an inverse process model to linearize and decouple the control loop;and training of nonlinear complex controllers needing high computation effort into a memory to allow very fast control action generation.
A general strategy for designing modular learning systems is to treat the problem as one of combining multiple models, each of which is defined over a local region of the input space. Jacobs, Jordan, Nowlan and Hinton...
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A general strategy for designing modular learning systems is to treat the problem as one of combining multiple models, each of which is defined over a local region of the input space. Jacobs, Jordan, Nowlan and Hinton introduced such a strategy with their 'mixture of experts' (ME) architecture for supervised learning. The ME architecture is closely related to the decision tree and multivariate spline algorithms. The gradient approach for training a mixture of experts architecture did not appear to take advantage of the modularity of the architecture. The Expectation-Maximization (EM) algorithm has been proposed for a mixture of experts architecture.
Simulations performed demonstrated the following qualitative difference between the regular Backpropagation (BP) and the algorithm with averaging. The trajectory of the averaged estimates is `smoother' because it ...
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Simulations performed demonstrated the following qualitative difference between the regular Backpropagation (BP) and the algorithm with averaging. The trajectory of the averaged estimates is `smoother' because it takes prehistory into account by using arithmetic mean of preceding points. Also, the same reason makes the averaged estimates converge faster when in the neighborhood of the solution. On the other hand, utilization of nonoptimaly decreasing stepsize γn in the basic algorithm slows down attainability of the neighborhood by the method with averaging.
Owing to advances in many technologies, the high-speed flywheel energy storage system (FESS), flywheel battery, has become a viable alternative to electrochemical batteries and attracted much research attention in rec...
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Owing to advances in many technologies, the high-speed flywheel energy storage system (FESS), flywheel battery, has become a viable alternative to electrochemical batteries and attracted much research attention in recent years. A self-organising fuzzy neural network controller is presented for FESS to improve transient stability and increase transfer capability of power systems. The main difference from a traditional control approach ties in the model-free description of the control system and parallel computing capability. Simulation results from the Taiwan power system (Taipower) show that FESS with the proposed controller has produced significant improvement in power system performance.
In this paper, we prove the stability of a certain class of nonlinear discrete MIMO (Multi-Input Multi-Output) systemscontrolled by a multilayer neural net with a simple weight adaptation strategy. The proof is based...
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In this paper, we prove the stability of a certain class of nonlinear discrete MIMO (Multi-Input Multi-Output) systemscontrolled by a multilayer neural net with a simple weight adaptation strategy. The proof is based on the Lyapunov formalism. The stability statement is, however, only valid if the initial weight values are not too far from their optimal values that allow perfect model matching. We therefore propose to initialize the weights with values that solve the linear problem. This extends our previous work (Renders, 1993;Saerens, Renders & Bersini. 1993), where single-input single-output (SISO) systems were considered.
The paper discusses modeling problems where there are some empirical data, and some limited system knowledge available. In such cases, first principles modeling may lead to an inaccurate model. Using black-box alterna...
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The paper discusses modeling problems where there are some empirical data, and some limited system knowledge available. In such cases, first principles modeling may lead to an inaccurate model. Using black-box alternative, not much of the system knowledge can be incorporated a priori. The authors propose an approach based on the observation that for many systems, adequate models of the behavior within small operating regimes can be found without too much difficulty, while global models, covering all possible operating conditions, tend to be very complex and difficult or expensive to build. The method constructs local models for the various operating regimes, and builds a global model by interpolating the local models.
proceedings of the colloquium on Computing and control Division 'neural and fuzzy systems: design, hardware and applications' (9 May 1997, Savoy Place, London) are presented. Nine reports were discussed. The m...
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proceedings of the colloquium on Computing and control Division 'neural and fuzzy systems: design, hardware and applications' (9 May 1997, Savoy Place, London) are presented. Nine reports were discussed. The main topics were the following ones: neural network and fuzzy system hardware implementation, multilayer feedforward neuralnetworks, hardware implementation of neuro-fuzzy systems;neuralnetworks with intrinsic learning behaviours;neuro-fuzzy network applications.
It has long been known that the optimal equalization of communication channels is achieved using the Maximum Likelihood Sequence Estimation (MLSE). The use of nonlinear clustering has been found to extend the capabili...
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It has long been known that the optimal equalization of communication channels is achieved using the Maximum Likelihood Sequence Estimation (MLSE). The use of nonlinear clustering has been found to extend the capabilities of MLSE in the case of channel nonlinearity or time variation. However, a problem with these techniques has been either high computational complexity of uncertainty about model order. The paper provides a technique which fixes the model order at an a-priori known level and simplifies the clustering by the use of a finite polynomial interpretation which also leads to simple and stable adaptation.
A Kohonen self organizing map was used to classify the reflectance spectra of a car body steel. The method produces more accurate results of corrosion evaluation than the spectroscopic corrosion index. Another advanta...
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A Kohonen self organizing map was used to classify the reflectance spectra of a car body steel. The method produces more accurate results of corrosion evaluation than the spectroscopic corrosion index. Another advantage of applying a Kohonen net for the classification is the more robust behavior towards spectral noise in the data.
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