An intelligent system for risk classification of stock investment projects

The proposed paper demonstrates that a hybrid fuzzy neural network can serve as a risk classifier of stock investment projects. The training algorithm for the regular part of the network is based on bidirectional incremental evolution proving more efficient than direct evolution. The approach is compared with other crisp and soft investment appraisal and trading techniques, while building a multimodel domain representation for an intelligent decision support system. Thus the advantages of each model are utilised while looking at the investment problem from different perspectives. The empirical results are based on UK companies traded on the London Stock Exchange

Some aspects of an evolvable hardware approach for multiple-valued combinational circuit design

In this paper a gate-level evolvable hardware technique for designing multiple-valued (MV) combinational circuits is proposed for the first time. In comparison with the decomposition techniques used for synthesis of combinational circuits previously employed, this new approach is easily adapted for the different types of MV gates associated with operations corresponding to different algebra types and can include other more complex logical expressions (e.g. singlecontrol MV multiplexer called T-gate). The technique is based on evolving the functionality and connectivity of a rectangular array of logic cells. The experimental results show how the success of genetic algorithm depends on the number of columns, the number of rows in circuit structure and levels-back parameter (the number of columns to the left of current cell to which cell input may be connected). We show that the choice of the set of MV gates used radically affects the chances of successful evolution (in terms of number of 100% functional solutions found)