Method representation in most object-oriented programing languages is too procedural and less declarative and expressive. Logic programming with declarative semantics can contribute a lot to the expressiveness of representing methods. Therefore, it is very desirable to combine logic programming and object-oriented programming to get the advantages of both. LogiC++ that integrates logic and object-oriented programming is designed primarily based on C++. However, methods in a LogiC++ program can be represented by Prolog Horn clauses. In this paper, we describe a compiler that takes a LogiC++ program as input and produces an equivalent C++ program as the output. The C++ program can then be compiled by a C++ compiler.
Time series models have been applied to forecast the market trends. Simple exponential smoothing (SES) method was one of them widely used as a forecasting tool in time series data. In this method, a smoothing parameter needs to be chosen in such a way tO minimize sums of squares forecast errors. By deriving the equivalence of the smoothing equation and the artificial neural network algorithm, we have shown that SES is equivalent to a special case of artificial neural network (ANN). Furthermore. we propose an adaptive simple exponential forecasting (ASES) method which merges SES and ANN to selfmodify the weighted connection and to obtain the estimate of the smoothing parameter for better forecasting. Both SES and ASES have been applied to the Standard and Poor 500 composite indexes of past twenty years in stock-market forecasting. ASES is generally superior to SES in forecasting.
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