This paper employs the continuous-time analogue Hopfield neural network to compute the temperature distribution in forward heat conduction problems and solves inverse heat conduction problems by using a back propagation neural (BPN) network to identify the unknown boundary conditions. The weak generalization capacity of BPN networks is improved by employing the Bayesian regularization algorithm...

In the study, embedded BASIC Stamp 2 (BS2) microchip controller is used to design with Hopfield neural network (HNN) as the foundation of sample training, which applies for a soldering platform of mechanical vision and accomplishes PCB soldering positioning technology. The proposed system design method in this paper can be divided into two parts: 1) the control rules of RC servo motor is design...

This paper presents a method for training a binary Hopfield neural network so that its energy function represents the fitness surface of an optimization problem with one or more target solutions. The main advantage of this method is that once the network has been trained, new solutions to a problem can be generated without reference to the original fitness function (which may take time to run)....

In this paper, we present an neural network approach to solve a set of nonlinear equations. A modified Hopfield network has been developed to optimize a energy function. This approach provides faster convergence and extremely accurate solutions for all solvable systems. We solved and discussed several illustrative examples in order to depict the powerfulness of the proposed method.

The effect of noise degradation on the Hopfield neural netwerk is s t u d i e d The notion of a hysteresis nefwork is defined. A noisy HephsSi =urd network is subsequently proven to be a hysteresis network. The effect of the hysteresis phentmenon on the robustness of the HoppeM neural network to noise degradation is then investigated. An eptiraal Heefield neural network is defined as the Hopfie...

This paper proposes an augmented Lagrangian Hopfield network (ALHN) for combined heat and power economic dispatch (CHPED) problem. The ALHN is the continuous Hopfield neural network based on augmented Lagrangian relaxation as its energy function. In the proposed ALHN, its energy function is augmented by Hopfield terms from Hopfield neural network and penalty factors from augmented Lagrangian re...