By Mauro L. Beretta, Andrea G. B. Tettamanzi (auth.), Prof. Andrea Bonarini, Prof. Francesco Masulli, Dr. Gabriella Pasi (eds.)
The papers accrued during this ebook are desirous about the appliance of the so-called "soft-computing" suggestions to the purpose of defining versatile structures. the themes coated witness the particular study development in the direction of an integration of distinctive formal thoughts for outlining versatile structures. The contributions during this quantity in most cases crisis the definition of platforms in different software fields, similar to photograph processing, regulate, asset allocation, medication, time sequence forecasting, qualitative modeling, help to layout, reliability research, prognosis, filtering, info research, land mines detection and so on. The papers offered during this quantity are equipped into 3 major thematic sections: Fuzzy structures, Neural Networks and Genetic and Evolutionary Algorithms, even if, as defined earlier than, a few works hire a couple of approach from those fields.
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Ond ending 3D c:oad:ine:res: of the endeffeci:lr tmjectory x, 7 3D cootdimte cfthe Elbow tmjectmyoftle ellxlwat 10 time step; p>q31-~ Biomechanic model to mannequin Fig. 3. Architecture of the NN system The kernel of the network is a multilayer perceptron (MLP) (see Fig. 3a). Its input is one of the 3D coordinates (x, or y, or z) of the point 54 from which the subject started the movements (A orB in figure 2) and the target point (C or D or E in figure 2), which the subject reached before getting back to the starting point.
Therefore the application of a sequence of bit-wise logical operators to an integer can be seen as a parallel execution of a program on N 1-bit operands in parallel, according to the Single Instruction Multiple Data (SIMD) paradigm. Such an approach can speed up Genetic Programming (GP) by making it possible to evaluate several fitness cases at the same time, if the teaching input of the fitness cases is binary. When the input patterns of the fitness cases are long bit strings, SmcGP also makes it possible to process anN-bit slice of each input at the same time.
For this reason, as a first step in the evaluation of SmcGP potentials in character classification, only the digit classifier has been reimplemented. The problem that has been tackled is therefore a 1Ocategory classification problem. Classification strategy A set of 10 binary classifiers, corresponding to each of the 10 decimal digits, has been developed. The output of each classifier is 1, when the input pattern represents the corresponding digit, or 0 otherwise. The final classification can be derived from the analysis of the response of all classifiers.
