Miroslaw Galicki

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In this study a generalised dynamic neural network (GDNN) was designed to process gait analysis parameters to evaluate equinus deformity in ambulatory children with cerebral palsy. The aim was to differentiate dynamic calf muscle tightness from fixed muscle contracture. Patients underwent clinical examination and had instrumented gait analysis before(More)
A method for the construction of optimal structures for feedforward neural networks is introduced. On the basis of a construction of a graph of network structures and an evaluation value which is assigned to each of them, an heuristic search algorithm can be installed on this graph. The application of the A*-algorithm ensures, in theory, both the optimality(More)
Theoretical investigations of time-optimal control of kinematically redundant manipulators subject to control and state constraints are presented in this work. The task is to move the end-effector along a prescribed geometric path (state equality constraints). In order to address a structure of time-optimal control, the concept of a regular trajectory(More)
This paper reviews the application of continuous recurrent neural networks with time-varying weights to pattern recognition tasks in medicine. A general learning algorithm based on Pontryagin's maximum principle is recapitulated, and possibilities of improving the generalization capabilities of these networks are given. The effectiveness of the methods is(More)
This paper is concerned with a general learning (with arbitrary criterion and state-dependent constraints) of continuous trajectories by means of recurrent neural networks with time-varying weights. The learning process is transformed into an optimal control framework, where the weights to be found are treated as controls. A new learning algorithm based on(More)
This paper addresses the problem of training trajectories by means of continuous recurrent neural networks whose feedforward parts are multilayer perceptrons. Such networks can approximate a general nonlinear dynamic system with arbitrary accuracy. The learning process is transformed into an optimal control framework where the weights are the controls to be(More)