One of the most widely used neural networks (NN) is the feedforward neural network (FNN). The most frequent application of FNN is in recognizing nonlinear patterns and, as a nonparametric method, in the estimation of functions especially in forecasting. In this study we will attempt to illustrate how a new metaheuristic algorithm known as Unconscious Search (US) may be utilized to train any feedforward neural network. US operates via a multi-start, memory-based, structured search algorithm that simulates the psychoanalytic psychotherapy process. The Theory of Psychoanalysis, propounded by Sigmund Freud is generally recognized as a descriptive and highly objective account of the mechanisms involved in psychological processes. This paper describes an analogy between the practice of psychoanalysis and the treatment of optimization problems, and it is the task of the present paper to apply US to the problem of training neural network. For this purpose we will first introduce US briefly then an application of US in training FNN is proposed and two benchmark problems are solved and the results of US are compared with the results of other metaheuristic algorithms.

This paper deals with the online path planning of mobile robots. We first suggest a systematic method to incrementally construct the Medial Axis of the workspace. This is done by using sensor information for land-marking the nodes of medial axis, which will guide the robot to explore the unknown environment thoroughly. Next, this approach is implemented in an online motion planning algorithm. This method can be generalized to higher spaces, and uses only the line of sight information of sonar sensors. It is much simpler than HGVG method, and is complete. The simulations showed good results for different environments.