abstract-manual harvesting of saffron as a laborious and exhausting job; it not only raises production costs, but also reduces the quality due to contaminations. saffron quality could be enhanced if automated harvesting is substituted. as the main step towards designing a saffron harvester robot, an appropriate algorithm was developed in this study based on image processing techniques to recogn...

we establish a relationship between general constrained pseudoconvex optimization problems and globally projected dynamical systems. a corresponding novel neural network model, which is globally convergent and stable in the sense of lyapunov, is proposed. both theoretical and numerical approaches are considered. numerical simulations for three constrained nonlinear optimization problems a...

as is well known, estimating parameters of the tree-parameter weibull distribution is a complicated task and sometimes contentious area with several methods vying for recognition. weibull distribution involves in reliability studies frequently and has many applications in engineering. however estimating the parameters of weibull distribution is crucial in classical ways. this distribution has t...

this article deals with the issues associated with developing a new design methodology for the nonlinear model-predictive control (mpc) of a chemical plant. a combination of multiple neural networks is selected and used to model a nonlinear multi-input multi-output (mimo) process with time delays.  an optimization procedure for a neural mpc algorithm based on this model is then developed. the p...

this paper investigates the performances of artificial neural networks approximation, the translog and the fourier flexible functional forms for the cost function, when different production technologies are used. using simulated data bases, the author provides a comparison in terms of capability to reproduce input demands and in terms of the corresponding input elasticities of substitution esti...

Field of system identification have become important discipline. Identification is basically the process of developing or improving a mathematical representation of a physical system using experimental data. The artificial neural network is a newly developed technique among the identification methods. Dynamic function mapping, including the structural dynamic model is still a challenging topic ...

In this paper, the design of dynamic neural networks with Kalman filter is proposed and applied to identify nonlinear dynamic systems. The optimal parameters of a dynamic neural network, which contains several autoregressive moving average (ARMA) sub models, weighs and biases, are obtained using the well known delta rule. Using the obtained parameters of the ARMA sub models, a new dynamic netwo...

Recently, dynamic pricing has been a common competitive maneuver in e-commerce. In many industries, firms adjust the product price dynamically by the current product inventory and the future demand distribution. In this paper, we used particle swarm optimization (PSO) algorithm to train neural networks, then introduced the PSO-trained neural network into e-commerce and presented a new dynamic p...

This paper explores training and initialization aspects of dynamic neural networks when applied to the nonlinear system identification problem. A well known dynamic neural network structure contains both output states and hidden states. Output states are related to the outputs of the system represented by the network. Hidden states are particularly important in allowing dynamic neural networks ...

In this paper, we propose a fast winner-take-all (WTA) neural network. The fast winner-take-all neural network with the dynamic ratio in mutual-inhibition is developed from the general mean-based neural network (GEMNET), which adopts the mean of the active neurons as the threshold of mutual inhibition. Furthermore, the other winner-take-all neural network enhances the convergence speed to becom...