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Full-Text Articles in Physical Sciences and Mathematics
Multivalued Approach For Uncertainty Management., Deba Prasad Mandal Dr.
Multivalued Approach For Uncertainty Management., Deba Prasad Mandal Dr.
Doctoral Theses
Real life problems are rarely free from uncertainty which usually emerges from the deficiencies of information available from a situation. The defi- ciencies may result from incomplete, imprecise, not fully reliable, vague or contradictory information depending on the problem. Management of uncer- tainty in a decision making system has been an important research problem for many years.Until the inception of the concept of fuzzy set theory in 1965 (1), the theory of probability and statistics was the primary mathematical tool for modeling uncertainty in a system/situation. Fuzzy set theory has shown enormous proinise in handling uncertaintics to a reasonable extent …
Applications Of Fuzzy Counterpropagation Neural Networks To Non-Linear Function Approximation And Background Noise Elimination, I. M. Wiryana
Applications Of Fuzzy Counterpropagation Neural Networks To Non-Linear Function Approximation And Background Noise Elimination, I. M. Wiryana
Theses: Doctorates and Masters
An adaptive filter which can operate in an unknown environment by performing a learning mechanism that is suitable for the speech enhancement process. This research develops a novel ANN model which incorporates the fuzzy set approach and which can perform a non-linear function approximation. The model is used as the basic structure of an adaptive filter. The learning capability of ANN is expected to be able to reduce the development time and cost of the designing adaptive filters based on fuzzy set approach. A combination of both techniques may result in a learnable system that can tackle the vagueness problem …
A Model Of Visual Recognition Implemented Using Neural Networks, Vincent C. Phillips
A Model Of Visual Recognition Implemented Using Neural Networks, Vincent C. Phillips
Theses: Doctorates and Masters
The ability to recognise and classify objects in the environment is an important property of biological vision. It is highly desirable that artificial vision systems also have this ability. This thesis documents research into the use of artificial neural networks to implement a prototype model of visual object recognition. The prototype model, describing a computtional architecture, is derived from relevant physiological and psychological data, and attempts to resolve the use of structural decomposition and invariant feature detection. To validate the research a partial implementation of the model has been constructed using multiple neural networks. A linear feed-forward network performs pre-procesing …