English title

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Handwritten digit recognition can be categorized as a classification problem. Probabilistic Neural Network (PNN) is one of the most effective and useful classifiers, which works based on Bayesian rule. In this paper, in order to recognize Persian (Farsi) handwritten digit recognition, a combination of intelligent clustering method and PNN has been utilized. Hoda database, which includes 80000 Persian handwritten digit images, has been used to evaluate our proposed classifier. Obtained results show that PNN is a powerful classifier and excellent choice for classification of Persian handwritten digits. Correct recognition rate when training and testing data have been used directly (without clustering) for training data is 100% and for testing data is 96%, but when k-means has been used as cluster tool and clusters' center have been used as training data, in this case, correct recognition rate for training data is 100% and for testing data is 96.16%. In addition, when Particle Swarm Optimization (PSO) has been used to find optimum clusters for each class of Persian handwritten digits, correct recognition rate in training data is 100% and for the testing data it reaches to 98.18%.

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Induction motors are so important in industry, so their protection and maintenance seem a vital issue. Continuous control of structural parameter of such motors is the way that can protect them. Appearance of a small problem in motor can change the value of structural parameter of    squirrel cage induction motor, such as; resistances of stator and rotor, inductances of stator and rotor and mutual inductance. Therefore, precise estimation of these structural parameters with acceptable reliability can help to control condition of motor. In this paper, in order to estimate the structural parameters of under studied induction motor, gray box identification procedure has been used, such that using extracted data from motor (namely; values of root mean square of stator current and power factor) and meta-heuristic algorithms, which are Particle Swarm Optimization (PSO), Improved version of Particle Swarm Optimization (IPSO), Gravitational Search Algorithm (GSA), Improved version of Gravitational Search Algorithm (IGSA), Harmony Search (HS) and Simulated Annealing (SA), a model of under studied induction motor is identified. Obtained results show that meta-heuristic optimization algorithms can be a good chose for estimating the parameters of induction motor. Assessments show that implementing tradeoff between speed, accuracy and reliability based on the user’s requirement best meta-heuristic algorithm can be selected.     

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Due to the wide range of Persian (Farsi) machine-printed sub-words, finding a sub-word and consequently a word in a machine-printed text will be very time consuming. In this paper, a biometric minutia based method is presented, which significantly reduces the search space of Farsi sub-words. Therefore, the number of points and their coordinates of bifurcated and ending minutia points, which are two well-known features in the field of biometrics, have been used as features to reduce the search space in the form of a two-step method. In the first step, the minutia points are extracted from the sub-word image and categorized into four clusters that are close and similar as a viewpoint of the number of minutia points. Therefore, search space will be halved by this step. In the second step, by creating a repository of the distances between the first and the last end points for each sub-word in each cluster and matching the same distance of experimental image with the repository, the search space is significantly reduced. Obtained results show that the search space has been reduced from 12,700 sub-words to about 500 sub-keywords with an accuracy of approximately 90%.