An intelligent robot has the ability to accomplish its specific task in the presence of uncertainty and variability in its environment and to adjust its actions based on what it has sensed. An artificial neural networ...
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The control of depth of anesthesia using artificial neural networks (ANNs) is discussed. The backpropagation algorithm is used to train the network on surgical data. The same technique is used to simulate a model of a...
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Two distinctive approaches to the implementation of approximate reasoning in rule-based fuzzy decision-making and controlsystems are presented. While the first approach, based on possibility theory, bears some resemb...
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Two distinctive approaches to the implementation of approximate reasoning in rule-based fuzzy decision-making and controlsystems are presented. While the first approach, based on possibility theory, bears some resemblance to a traditional event-driven inference system with the incorporation of fuzzy concepts, the second scheme, based on the technique of neural networks, represents a substantial departure from the traditional one and shows some promise in dealing with these fundamental issues. To demonstrate the applicability of the proposed approaches, a problem of multivariable fuzzy management of blood pressure has been studied using the simulation method.< >
The authors present a system identification scheme and an adaptive control algorithm for a class of nonlinear systems, based on the computational properties of artificial neural network models. An estimation procedure...
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The authors present a system identification scheme and an adaptive control algorithm for a class of nonlinear systems, based on the computational properties of artificial neural network models. An estimation procedure for the link parameters is described in which identification is carried out using the parallel recursive prediction error technique. The algorithm enables the weights in each neuron of the network to be updated in an efficient parallel manner and has better convergence than the classical backpropagation algorithm. The whole of the algorithm can be distributed over a network of parallel processors to achieve impressive speedup. An example is given for the first three links of the Stanford arm to demonstrate the effectiveness of this algorithm for the cases of dense gain matrix and diagonal gain matrix.< >
Viewing the given rule-base as defining a global linguistic association constrained by fuzzy sets, approximate reasoning is implemented by a backpropagation neural network (BNN) with the aid of the fuzzy set theory. T...
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Viewing the given rule-base as defining a global linguistic association constrained by fuzzy sets, approximate reasoning is implemented by a backpropagation neural network (BNN) with the aid of the fuzzy set theory. The underlying principles are examined in detail using two examples, paying particular attention to the capability of generalization of the BNN. The simulation results indicate the feasibility of the BNN-based approach. It is demonstrated that a forward-chaining fuzzy reasoning system with parallel rule-bases can be implemented within the framework of neural networks. The studies into the BNN-based fuzzy controller suggest that, besides a seeming resemblance between rules and patterns in the logic-based and BNN-based approaches, there exists a deeper similarity in the information processing aspect in them, namely, fuzziness vs. distributiveness.< >
The motion of mining machines is examined by analogy to that of spacecraft, aircraft and submarines. Simulation of mining machine steering dynamics is an essential prerequisite to the proper design of guidance systems...
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The motion of mining machines is examined by analogy to that of spacecraft, aircraft and submarines. Simulation of mining machine steering dynamics is an essential prerequisite to the proper design of guidance systems, whether manual or automatic, with respect whether to laser target beams or to meandering geological features. Mining excavation machines exhibit long-term dynamic behaviour that must be properly analysed and understood before successful simulation can be accomplished. Although slow in their effects, these dynamics are quite as powerful as are aerodynamics on the flight characteristics of aircraft. Because of the solid environment in which the mining machines operate, the physical causes of pitch, yaw and roll are different, but the resultant dynamical equations can show some similarity. However, additional effects remain that pose unique control design problems. The mining machine is perhaps best regarded a a mobile robot that constantly recreates its own environment or workcell.< >
The major roles which are the concern of a clinical anaesthetist are those of drug-induced unconsciousness, muscle relaxation, and analgesia (ie pain relief). The first two roles are concentrated in the operating thea...
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The major roles which are the concern of a clinical anaesthetist are those of drug-induced unconsciousness, muscle relaxation, and analgesia (ie pain relief). The first two roles are concentrated in the operating theatre, whereas the third role is mainly concentrated in post-operative conditions. Each of these roles has been researched in recent years for the possibility of automated drug-infusion via feedback strategies, but only the former two will be considered in this paper.
The control of depth of anesthesia using artificial neural networks (ANNs) is discussed. The backpropagation algorithm is used to train the network on surgical data. The same technique is used to simulate a model of a...
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The control of depth of anesthesia using artificial neural networks (ANNs) is discussed. The backpropagation algorithm is used to train the network on surgical data. The same technique is used to simulate a model of a patient under the effect of an anesthetic agent. An alternate controller and a patient model (PM) are developed by means of regression analysis of surgical data. The ANN controller and the ANN-PM are studied under closed-loop conditions, and the results are compared with those obtained by regression.< >
control may be carried out using compact mathematical models which describe the thermal behaviour of building systems. In this paper, the performance of advanced stochastic model-based control algorithms for use in bu...
Taking account of the fuzzy nature of human decision making processes and real-time properties, this paper establishes a unified approximate reasoning model based on possibility theory rather than on relational matrix...
Taking account of the fuzzy nature of human decision making processes and real-time properties, this paper establishes a unified approximate reasoning model based on possibility theory rather than on relational matrix computation. Both fuzzy and random uncertainties can be coped with in the model. In the case of sensor-based situations, a simpler reasoning scheme is derived by introducing the concepts of matching measures. The proposed models may provide another possibility for on-line reasoning in real-time expert system applications. In Part 1, the theoretical foundations are provided, while in Part 2 their application to multivariate blood pressure control is described.
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