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The purpose of this study is modeling and the effect of economic shocks on stock returns of food industry during the period 2009 to 2020. In this research, the optimal macroeconomic variables on the stock returns of food companies are modeled using the genetic algorithm approximation function method and then the impulses and shocks of macroeconomic variables affecting the stock returns of food industriesanalyzed by Auto regression method. Initially, using the genetic function approximation algorithm, four variables of OPEC oil price, liquidity volume, land price and stock price index were identified among the eight macroeconomic variables as influential variables in the optimal regression model. OPEC oil prices and land prices have a negative and significant effect on the stock returns of food companies, while the volume of liquidity and stock price index have a positive and significant effect on the stock returns of food companies. Given the response impulse functions, the stock return reaction to OPEC oil prices and liquidity was initially positive. In the analysis of variance method, the largest share is due to the shock of the food industry stock returns to itself, followed by the volume of liquidity. Given the positive impact of liquidity on the stock returns of food industry companies, it is suggested that policy makers and planners to implement policies to increase the volume of liquidity to food industry companies in order to develop food industry investment. [ABSTRACT FROM AUTHOR]

In this paper, a new method of data-driven controller (DDC) design using Simultaneous Perturbation Stochastic Approximation Algorithm (SPSA) and Neural Network (NN) training is presented. This method can be used to control a variety of linear and nonlinear systems. In the simultaneous perturbation stochastic approximation algorithm, the controller is assumed to have a fixed structure and its parameters must be estimated. In this paper, a Proportional, Integral, and Derivative controller (PID) is considered and the parameters that should be estimated by the proposed algorithm are proportional, integral and derivative terms of this controller. In the proposed method, the simultaneous perturbation stochastic approximation algorithm is quantified by using neural network training which increases the convergence speed and also improves the performance of the algorithm against system input changes. Simulations performed on cement grinding particle size distribution process and pitch angle control of aircraft show the high efficiency and potential of the proposed method. [ABSTRACT FROM AUTHOR]