This paper describes an example of how fuzzy systems can intergrate with neural networks and what benefits can be obtained from the combination. In particular, by drawing some equivalence between a simplified fuzzy co...
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A new neural hierarchy is proposed for the recognition of on-line handwritten alphanumeric and mathematical symbols. The neural hierarchy forms part of a novel mathematical editor, that uses handwriting as the princip...
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In 1939, the Taylor Instrument Companies introduced a completely redesigned version of its Fulscope pneumatic controller. In addition to proportional and reset control actions, this new instrument provided an action w...
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In 1939, the Taylor Instrument Companies introduced a completely redesigned version of its Fulscope pneumatic controller. In addition to proportional and reset control actions, this new instrument provided an action which the Taylor Instrument Companies called pre-act. In the same year the Foxboro Instrument Company added Hyper-reset to the proportional and reset control actions provided by their Stabilog pneumatic controller. The pre-act and Hyper-reset actions each provide a control action proportional to the derivative of the error signal. Reset provides a control action proportional to the integral of the error signal and hence both controllers offered PID control. The historical development of these controllers is discussed.< >
Multiobjective genetic algorithms (MOGAs) are introduced as a modification of the standard genetic algorithm at the selection level. Rank-based fitness assignment and the implementation of sharing in the objective val...
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Multiobjective genetic algorithms (MOGAs) are introduced as a modification of the standard genetic algorithm at the selection level. Rank-based fitness assignment and the implementation of sharing in the objective value domain are two of the important aspects of this class of algorithms. The ability of the decision maker (DM) to progressively articulate its preferences while learning about the problem under consideration is one of their most attractive features. Illustrative results of how the DM can interact with the genetic algorithm are presented. They also show the ability of the MOGA to uniformly sample regions of the trade-off surface.< >
An example of how fuzzy systems can integrate with neural networks and what benefits can be obtained from the combination is described. By drawing some equivalence between a simplified fuzzy control algorithm (SFCA) a...
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An example of how fuzzy systems can integrate with neural networks and what benefits can be obtained from the combination is described. By drawing some equivalence between a simplified fuzzy control algorithm (SFCA) and radial basis functions (RBF) networks it is concluded that the RBF network can be interpreted in the context of fuzzy systems and can be naturally fuzzified into a class of more general networks, referred to as FBFN. The FBFN is used as multivariable rule-based controller with the ability of self-constructing its own rule-base by incorporating an iterative learning control algorithm into the system. The approach is applied to a problem of multivariable blood pressure control with a FBFN-based controller having six inputs and two outputs, representing a complicated control structure.< >
The traditional approach to multiple parameter optimization in genetic algorithm (GA) practice is to combine the coding of the parameters into a single compound bit-string; the so-called concatenated binary mapping. T...
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The traditional approach to multiple parameter optimization in genetic algorithm (GA) practice is to combine the coding of the parameters into a single compound bit-string; the so-called concatenated binary mapping. This approach has some shortcomings; the GA is a competition-based technique that has a natural tendency to evolve one winner which in complex problems yields a solution that is better on some parameters than the others. An extension to the simple GA, called vector evaluated genetic algorithm (VEGA), has been used in multiobjective optimization where one is not interested in a single solution, but a family of optimal solutions. In VEGA each member of the population is evaluated and assigned a weighted fitness value dependent on how it relates to each objective criteria. The reproduction plan then develops groupings within the populations for each of the objectives to be optimized, ensuring that the improvement of one objective does not adversely affect the others. This, however, requires large population sizes and can be quite inefficient. In cases where the complex task is divisible into simpler optimization problems, a better solution set may be obtained using parallel genetic algorithms to search for the optimal solution to each sub-problem.< >
This work is concerned with the validation of reduced-order models which are intended for closed-loop applications. The main objective is to point out some issues arising in the use of such models in closed-loop appli...
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This work is concerned with the validation of reduced-order models which are intended for closed-loop applications. The main objective is to point out some issues arising in the use of such models in closed-loop applications. It is shown that in validating simplified models, special attention should be given to the accuracy of the approximation at crossover frequencies. This observation naturally leads to the conclusion that, in some cases, open-loop model reduction techniques can be successfully used in deriving models intended for closed-loop applications thus avoiding optimal methods which are far more time-consuming. A second objective is to show how a reduced-order model could have varying degrees of accuracy in closed-loop when controller parameters are varied. Numerical examples which use the model of an actual fuel control system are included to illustrate the main points of the paper.< >
The authors present a fuzzified cerebellar model articulation controller (CMAC) network acting as a multivariable adaptive controller featuring self-organizing association cells and the ability for self-learning requi...
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The authors present a fuzzified cerebellar model articulation controller (CMAC) network acting as a multivariable adaptive controller featuring self-organizing association cells and the ability for self-learning required teaching signals in real time. In particular, the original CMAC has been reformulated within a framework of a simplified fuzzy control algorithm, and the associated self-learning algorithms have been developed by incorporating the schemes of competitive learning and iterative learning control into the system. The approach described here can be thought of as either a completely unsupervised fuzzy-neural control strategy or equivalently an automatic real-time knowledge acquisition scheme. The approach has been successfully applied to a problem of multivariable blood pressure control.< >
Genetic algorithms (GA) are adaptive search techniques, based on the principles of natural genetics and natural selection, which, in controlsystemsengineering, can be used as an optimization tool or as the basis of ...
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Genetic algorithms (GA) are adaptive search techniques, based on the principles of natural genetics and natural selection, which, in controlsystemsengineering, can be used as an optimization tool or as the basis of more general adaptive systems. Following an introduction to the simple GA, important characteristics of GA are identified and control applications are described.< >
A quantitative measure of modal dominance for continuous systems is proposed. This measure takes into account both transient and steady-state information of the system. Consequently the new indices indicate which pole...
A quantitative measure of modal dominance for continuous systems is proposed. This measure takes into account both transient and steady-state information of the system. Consequently the new indices indicate which poles are dominant even when they are not the slowest. Simple formulae are developed for transfer functions and state-space models. Three numerical examples illustrate the main points of the paper.< >
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