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Associative neural memories are models of biological phenomena that allow for the storage of pattern associations and the retrieval of the desired output pattern upon presentation of a possibly noisy or incomplete version of an input pattern. In this study, we introduce fuzzy swarm particle optimization technique for convergence of associative neural memories based on fuzzy set theory. A Fuzzy Particle Swarm Optimization (FPSO) consists of clustering of swarm's particle by applying fuzzy c-mean algorithm to attain the neighborhood best. We present a singular value decomposition method for the selection of efficient rule from a given rule base required to attain the global best. Finally, we illustrate the proposed method by virtue of some examples. Further, ant colony system ACS algorithm is used to study the Symmetric Traveling Salesman Problem TSP. The optimum parameters for this algorithm have to found by trial and error. The ACS parameters working in a designed subset of TSP instances has also been optimized by virtue of Particle Swarm Optimization PSO.

Stock market movements are affected by numerous factors making it one of the most challenging problems for forecasting. This article attempts to predict the direction of movement of stock and stock indices. The study uses three classifiers - Artificial Neural Network, Random Forest and Support Vector Machine with four different representation of inputs. First representation uses raw data (open, high, low, close and volume), The second uses ten features in the form of technical indicators generated by use of technical analysis. The third and fourth portrayal presents two different ways of converting the indicator data into discrete trend data. Experimental results suggest that for raw data support vector machine provides the best results. For other representations, there is no clear winner regarding models applied, but portrayal of data by the proposed approach gave best overall results for all the models and financial series. Consistency of the results highlight the importance of feature generation and right representation of dataset to machine learning techniques.

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