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Hybrid systems are a group of dynamical system which their behavior described by the interaction of discrete and continuous dynamical system behaviors. One of the subsets of hybrid systems, is piecewise affine system. Piecewise affine system identification, consists of estimating the parameters of each subsystem and the coefficients of the state-input boundary hyperplanes. In order to clustering the state-input space and estimating the feature matrixes simultaneously, bounded error algorithm and adaline neural network are used. It should be said that in this method, there is no need to know the number of linear subsystems of the piecewise affine system. Moreover, it should be noted that the identification method is extended based on on-line data acquisition from system. In continuation, this method is used to identify a benchmark mathematical piecewise affine system. By comparing the results with the reference paper, it is proven that this method has a good performance in clustering the state-input space and estimating the feature matrixes. In the end, by using the proposed method, an active water tank which its equations are described by the form of a piecewise affine system is identified.

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Identification and classification of signals which are heard by underwater microphones (hydrophones) can be used extensively in harbor traffic management, especially in economical harbors. However, automatic identification and classification of acoustic signals which are received by passive sonar system is a challenging problem, because of variation in temporal and frequency characteristics of signals (even they are received from a same source). In this paper, a novel method for classification of acoustic signals is presented, based on DWT as preprocessing, a diverse range of feature extraction methods (principal component analysis and its variations (6 methods) and discriminant analysis and its variations (3 methods)), and 4 ensemble learning methods with 3 classifiers (multilayer perceptron (MLP), probabilistic neural network (PNN) and support vector machine (SVM)). Performing a diverse range of performance tests, the performances of different methods are assessed and the best ones are chosen for the proposed method. The proposed method is used to extract features and classify acoustic signals of 8 ships. Using the proposed method, some real signals and their noisy version are classified. The accuracy of the proposed method in classification of test signals with Gaussian white noise with -5, -10 and -15 signal-to-noise ratio is obtained as 99.83%, 97.06% and 83.56%, respectively.

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Flight schedule design and fleet assignment are the main sub problems of the airline schedule planning which have the most effect on the costs and profit of the airline. In this paper, integrated flight schedule design and fleet assignment problem is described and genetic algorithm has been developed to solve this problem. It has numbers of constraints and multi-layer permutation chromosomes with variable length. So, creating the initial population randomly and use of customary operators of evolutionary algorithms will not be efficient since the probability of feasibility is very low. For this purpose, a new function based on loop concept to create an initial population and new crossover and mutation operators have been developed. A genetic algorithm has been used within the main loop to optimize the redirection of the passengers. Four models with different numbers of airports and fleets are created as an input for the problem which have been solved by two and three islands genetic algorithms. Results show that in each iteration of the main loop, feasible answers are obtained and finally there was a proper improvement in the costs. In larger models, there is a better Improvement in the costs and more difference between two and three islands algorithms. Three islands mode results in a better solution within a longer time. The developed algorithm can successfully find feasible optimal solution and it can be used for high-dimensional problems in which there is no possibility to find the optimal solution by using conventional methods such as MILP.