Provision of reliability of the walking robots is complex problem. Application of soft computing allows to provide the reliability of robots. Soft-computing is new discipline that bring together all features of fuzzy-...
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Provision of reliability of the walking robots is complex problem. Application of soft computing allows to provide the reliability of robots. Soft-computing is new discipline that bring together all features of fuzzy-logic, genetic programming, and neuralnetworks. The main peculiarity of soft-computing is capability to treat with uncertain systems that cannot be easily modelled an controlled by using the classical approaches. The walking robots are typical example of systems affected by uncertainty: leg kinematic is often non-linear and known with low accuracy. Overview of soft-computing techniques developed that have been applied to several walking robots is given.
Non-linear techniques have been developed for the purpose of filtering navigational data and control of ships. Recently, research at the Institute of Marine Studies into ship control employed artificial intelligence i...
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Non-linear techniques have been developed for the purpose of filtering navigational data and control of ships. Recently, research at the Institute of Marine Studies into ship control employed artificial intelligence in the form of neuralnetworks. Mathematical and scale models of the controlsystems are developed to provide an intelligent autopilot ship control capable of emulating the human operator.
We study the relation between neural network based control and nonlinear parametric adaptive control. We consider the static problem and give results which show that, for smooth systems, continuous stabilising control...
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We study the relation between neural network based control and nonlinear parametric adaptive control. We consider the static problem and give results which show that, for smooth systems, continuous stabilising controls imply the existence of neural network controls for compact sets of initial conditions. We discuss the possibilities for global results and show that this is possible in a very restrictive sense. controllability and observability are briefly considered. We illustrate a control problem (speed field tracking) which cannot even be approximately solved by neuralnetworks. These static results serve to illustrate the fundamental limitations of approximate parameterisations which will impose limitations on the adaptive schema. Finally, we illustrate the adaptive situation by a feedback linearisation scheme.
The contingent negative variation (CNV) is a cognitive event-related potential elicited by the presentation of a warning stimulus followed by an imperative stimulus. neural nets trained on CNV data offer an additional...
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The contingent negative variation (CNV) is a cognitive event-related potential elicited by the presentation of a warning stimulus followed by an imperative stimulus. neural nets trained on CNV data offer an additional tool for the diagnosis of Huntington's Disease, Parkinson's Disease and schizophrenia, and may also offer a means of detecting and monitoring the pre-onset of Huntington's Disease. In future, it may be possible to quantify and monitor symptoms and to sub-classify the conditions, thus leading to improved drug treatment and patient care. Application of the fuzzy ARTMAP is most promising in this domain.
There has been much work on self-tuning PID controllers. There are many possible structures for PID controllers. The basic concepts of PID control can be generalised within the same structure but allowing for the cont...
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There has been much work on self-tuning PID controllers. There are many possible structures for PID controllers. The basic concepts of PID control can be generalised within the same structure but allowing for the control of complicated dynamic systems using advanced control design algorithms. This structure arises naturally from the system description and does not need to be imposed artificially. The concept of PID control can be usefully generalised to make contact with recent methods such as internal-model control and generalised predictive control. One should not start with a PID controller and then decide how to tune it, but rather one should start with a rational design method and system model from which a (generalised) PID structure will then materialise. Recent advances in local model networks using multiple-model self-tuning PID controllers give a neat extension of the basic (generalised) PID structure to handle nonlinear or time-varying systems. The PID concept is alive and well.
A solution to symbol grounding problems has been proposed which abandons the symbol system altogether in favor of noncomputational systems. An alternative to abandoning symbols altogether is to abandon pure symbol sys...
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A solution to symbol grounding problems has been proposed which abandons the symbol system altogether in favor of noncomputational systems. An alternative to abandoning symbols altogether is to abandon pure symbol systems for hybrid symbolic/nonsymbolic systems in which the symbol-meaning connection is not interpretation-dependent but autonomous and direct.
Inferential estimation involves the determination of difficult to measure process variables from easily accessible secondary information. In addition to the capital costs of instrumentation, the downward pressure on m...
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Inferential estimation involves the determination of difficult to measure process variables from easily accessible secondary information. In addition to the capital costs of instrumentation, the downward pressure on manpower costs and overheads involved in maintenance make soft sensing attractive for industrial applications. Viscosity control in a polymerization reactor is addressed. The neural network based inferential estimation, where the network is trained to predict the polymer viscosity from past torque and viscosity data, is investigated. Results from the offline training of a feedforward network are presented and new work on online viscosity estimation using B-Spline networks is described.
The proceedings contain nine papers on unmanned and remotely operated vehicles. Topics include unmanned submarines and aircraft, remote control, controlsystems, neuralnetworks, flight dynamics, and navigation systems.
The proceedings contain nine papers on unmanned and remotely operated vehicles. Topics include unmanned submarines and aircraft, remote control, controlsystems, neuralnetworks, flight dynamics, and navigation systems.
The work reported here is concerned with research carried out at the University of Birmingham, UK and funded by London Underground Limited (LUL) into early-failure warning systems for safety-critical railway signallin...
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The work reported here is concerned with research carried out at the University of Birmingham, UK and funded by London Underground Limited (LUL) into early-failure warning systems for safety-critical railway signalling equipment. The paper outlines the motivation for the research, a brief overview of the requirements for condition monitoring systems, a summary of various condition monitoring and fault diagnosis used in the study. The process of laboratory tests and field trials which led to the development of ideas and techniques for the employment of intelligent neural network-based sensors for on-line condition monitoring of railway equipment are briefly reported. Problems still to be tackled will be addressed.
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