Background: Through the diagnostic decision support systems, potential patients or those who are on the threshold succumbing to a disease can be diagnosed early; thus, the prevention of unnecessary angiography for people not suffering from the coronaryartery disease as well as its dangers and costs can be avoided. The present study aimed at the efficiency evaluation of a multilayer perceptron neural network based on the number of hidden layers and nodes to diagnose coronary heart disease. Methods: A fundamental analysis was conducted on the provided data related to 13,228 patients who had undergone coronary angiography and the database (nine risk factors including age, gender, BMI, body fat, family history, smoking, blood cholesterol, diabetes, and high blood pressure) was investigated in this research using SPSS statistics (17.0) and R (2.13.2) software. In the next stage, through utilizing MATLAB (R2014a), 1332 different MLP neural networks were created. Results: Based on the largest area under the ROC curve, the best model of MLP neural network was selected involving two hidden layers; the first layer had 34 and the second one had 18 hidden nodes. This model had the highest efficiency of 82% in the diagnosis of coronary artery disease. Conclusions:The obtained results demonstrated that the MLP makes an acceptable approach to the diagnosis of coronary artery disease in patients without the need for performing angiography. The development of this model will result in creating an algorithm for decision support systems to diagnose coronary artery disease, as well.

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One of the classic systems in dynamics and control is the inverted pendulum, which is known as one of the topics in control engineering due to its properties such as nonlinearity and inherent instability. Different approaches are available to facilitate and automate the design of fuzzy control rules and their associated membership functions. Recently, different approaches have been developed to find the optimal fuzzy rule base system using genetic algorithm. The purpose of the proposed method is to set fuzzy rules and their membership function and the length of the learning process based on the use of a genetic algorithm. The results of the proposed method show that applying the integration of a genetic algorithm along with Mamdani fuzzy system can provide a suitable fuzzy controller to solve the problem of inverse pendulum control. The proposed method shows higher equilibrium speed and equilibrium quality compared to static fuzzy controllers without optimization. Using a fuzzy system in a dynamic inverted pendulum environment has better results compared to definite systems, and in addition, the optimization of the control parameters increases the quality of this model even beyond the simple case.