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Consideration of impreciseness is more realistic for modelling of physical phenomena. This impreciseness can be considered in several ways like, interval/stochastic/fuzzy or mixture of these. In this work, we have taken for optimising of the system reliability of a redundancy allocation problem formulated from a complex network system with imprecise parameters in the form of trapezoidal fuzzy numbers (TrFN). The signed distance method has been used to defuzzify the fuzzy values. Then big-M penalty technique is used to transform the problem to unconstrained optimisation problem. To solve these problems, we have implemented the real coded elitist genetic algorithm (RCEGA) for integer variables with tournament selection, intermediate crossover and one neighbourhood mutation. For illustration, the five link bridge network system has been solved and the results have been presented.

This study described the use of Kernel Least Square Tracker based estimation for 3-dimensional shoulder, elbow motion kinematics from surface Electromyogram (EMG) and a two-stage multiclass Support Vector Machine based classification of different wrist, grip and finger motions from Electroencephalogram (EEG). The advantage of employing hybrid EEG-EMG strategy for upper limb motion estimation was demonstrated for a transhumeral subject. The method utilized EMG from upper arm muscles for elbow motion (and shoulder motion in case of higher degree amputation scenario) and used EEG for discerning basic wrist, grip and finger motions. The results showed that the hybrid scheme could estimate shoulder, elbow motion with more than 90% accuracy and wrist, grip and finger motion with 65%-70% accuracy. This strategy of using hybrid EEG-EMG motion estimation, thus, could be employed in developing a more intuitive upper limb prosthesis controller with multiple degrees of freedom.