Combination Of Artificial Neural Network Research Articles

Recognition of hand-printed latin characters based on a structural approach with a neural network classifier

Character recognition systems can contribute tremendously to the advancement of the automation process and can improve the interaction between person and machine in many applications, including office automation, check verification, and a large variety of banking, business, and data entry applications. Our main theme is the automatic recognition of hand-printed Latin characters using artificial neural networks in combination with conventional techniques. This approach has a number of advantages: it combines rule-based (structural) and classification tests; it is more efficient for large and complex sets; and feature extraction is inexpensive and execution time is independent of handwriting style and size. The technique can be divided into three major steps. The first step is preprocessing in which the original image is transformed into a binary image utilizing a 300 dpi scanner and then thinned using a parallel thinning algorithm. Second, the image skeleton is traced from left to right to build a binary tree. Some primitives, such as straight lines, curves, and loops are extracted from the binary tree. Finally, a three layer artificial neural network is used for character classification. The system was tested on a sample of handwritten characters from several individuals whose writing ranged from acceptable to poor in quality and the correct recognition rate obtained was 91%.

Exploratory Analysis of Bioprocesses Using Artificial Neural Network‐Based Methods

AbstractA process data driven procedure has been developed that allows a universal time‐efficient bioprocess analysis. The procedure is particularly suited for industrial production processes which have not yet been comprehensively investigated. It makes use of artificial neural networks in combination with mass balance equations to represent the process dynamics on a commercial workstation. The essential concept behind the procedure is to start with the already available knowledge formulated by a very simple process representation which includes only those variables that are firmly known to be essential. Then, stepwise, additional variables are added to the basic representation after they passed a test procedure in which they proved to enhance the model's performance. The result of the procedure is a numerical representation of the important process relationships that immediately allows to determine improved set points and/or profiles for the manipulated variables with respect to process performance. It may be used to improve state estimation and control. The procedure has already been tested in industrial applications. In this paper, a validation of the procedure with simulated bioprocess data is presented.