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The assimilation of formal logic into the domain of Software Engineering offers the possibility of enormous benefits in terms of software reliability and verifiability. To date, however, the integration of such techniques has proved difficult since they involve a significantly increased burden on the programmer in meeting the demands of the formal mechanisms being employed. The current paper investigates the advantages which may be gained by the software development process with the introduction of Artificial Neural Network technology into a formal software development system. Essentially, the adaptive artificial neural network model is employed to refine an existing formal software model in order to produce increasingly better approximations to a given solution. Each approximation is itself a valid formal system whose precise behaviour may be formally determined. The paper introduces a framework by which a programmer may define a system possessing the abstract structure of a traditional neural network but whose internal structures are taken from the formal mathematical domain of Constructive Type Theory. The system will then refine itself to produce successive approximations to a desired goal based on data presented to it. An example is presented addressing a problem domain which has previously proved difficult to model. Although the example presented is necessarily limited, it does provide an insight into the potential advantages of merging formal logic with artificial neural systems. [ABSTRACT FROM AUTHOR]

In this paper, we provide a geometric construction of a transversal homoclinic orbit for a nonlinear model neuron with time delays. The existence of chaos for time-delay systems in a high-dimensional space is theoretically confirmed with several conditions. These units may be used as basic elements for networks with higher-order information processing capabilities. [ABSTRACT FROM AUTHOR]

The design of intelligent controllers for nonlinear systems continues to be a challenging problem, particularly when the system is uncertain or the environment noisy. A nonparametric approach which has gained success is to employ a neural network to learn about the unknown plant and fuzzy inference to compensate for the uncertainty (GANFIS control). Inherent in the design of such controllers is the need to tune the weights of the GANFIS controller. Evolutionary learning has been suggested to tune the GANFIS parameters but a difficulty is selecting the parameters for tuning. Further, it is well known that proper selection of the fitness function has an important effect on system performance. In this paper, we integrate two design techniques that we have previously developed into a single generalized ANFIS controller: adaptive tuners to select critical evolutionary parameters and a predictive fitness function for measuring system performance. The adaptive tuners also employ this predictive fitness as part of selection process which is a new approach. Results show that this approach is a feasible method in designing GANFIS controllers using evolutionary tuning and predictive fitness. [ABSTRACT FROM AUTHOR]