Common heat transfer fluids such as water, ethylene glycol, and engine oil have limited heat transfer capabilities due to their low heat transfer properties. Nanofluids are suspensions of nanoparticles in base fluids, a new challenge for thermal sciences provided by nanotechnology. In this study, we are to optimize and report the effects of various parameters such as the ratio of the thermal co...

Nonlinear system identification using recurrent neural network with genetic algorithm is presented. A continuous-time model of Hopfield neural network is used in this study. Its convergence properties are first evaluated. Then the model is implemented to identify nonlinear systems. Recurrent network‘s operational factors of the system identification scheme are obtained by genetic algorithm. Mat...

In this paper, a continuous time recurrent neural network (CTRNN) is developed to be used in nonlinear model predictive control (NMPC) context. The neural network represented in a general nonlinear statespace form is used to predict the future dynamic behavior of the nonlinear process in real time. An efficient training algorithm for the proposed network is developed using automatic differentia...

In this paper, a Hopfiled neural network for nonlinear constrained optimization problem is discussed. The energy function for the nonlinear neural network with its neural dynamics is defined based on penalty function with two-order continuous differential. The system of the neural network is stable, and its equilibrium point of the neural dynamics is also an approximately solution for nonlinear...

A new architecture for Dynamic Synapse Neural Networks (DSNNs) has been introduced based on incorporating a continuous nonlinear mechanism to simulate synaptic neurotransmitter release, adding a nonlinear output layer, and utilizing a Gauss-Newton learning method to train the network. We applied this network to simulate two nonlinear dynamical systems and then tried to identify the dynamical sy...

Reducing or eliminating statistical redundancy between the components of high-dimensional vector data enables a lower-dimensional representation without significant loss of information. Recognizing the limitations of principal component analysis (PCA), researchers in the statistics and neural network communities have developed nonlinear extensions of PCA. This article develops a local linear ap...

Convex optimization techniques are widely used in the design and analysis of communication systems and signal processing algorithms. In this paper a novel recurrent neural network is presented for solving nonlinear strongly convex equality constrained optimization problems. The proposed neural network is based on recursive quadratic programming for nonlinear optimization, in conjunction with ho...

This paper presents the results of applying two different types of neural networks in two different approaches to the sensor validation problem. The first approach uses a functional approximation neural network as part of a nonlinear observer in a modelbased approach to analytical redundancy. The second approach uses an auto-associative neural network to perform nonlinear principal component an...