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In this paper, the transmittance estimation of touch panel decoration film by using quantum neural network (QNN) is proposed. This model is able to catch the complex relationship between the film's transmittance and its possible influencing factors. An artificial intelligent (AI) mechanism for the decision of control parameters of film evaporation is expected to be developed. Based on this AI mechanism,...
This paper presents a technique, called “nearly equivalent neural network (NN) model” in the application of nonlinear system identification. This technique is expected to adequately to catch the behavior of the nonlinear system. To demonstrate the new technique proposed, the evaporation system of TP decoration film was analyzed. The complex relationship between the film's transmittance...
This paper presents a technique in how to searching the global minimum for the supervised neural network training. This technique is developed based on the idea of nearly equivalent model. To demonstrate the new technique proposed, two signal processing studies, including signal recognition and signal modeling were simulated. For a comparison, the same simulations were also performed by using the...
In this paper, a new logic circuit design technique by using neural network and particle swarm optimization (PSO) method is proposed. The neural network was used to substitute the logic unit and PSO algorithm was used to determine the possibility of connections among the logic units. By off-line gate-level samples, the simulation results clearly demonstrate the validity of this new technique. It could...
In this paper, an automatic quality inspection system for the riveting process by using quantum neural network (QNN) was proposed. This inspection system not only can monitor the real time riveting process, but also can give the assistance on the riveting quality verification. For demonstrating the superiority of the inspection system we developed, the data provided by the experiment did by Chinese...
In this paper, a transmittance estimator of touch panel decoration film by using neural network is presented. In the evaporation process, the coating material and the related control parameters are all important influencing factors in obtaining the desired transmittance. The relationship among the transmittance and these factors are very complex and nonlinear. It's very hard to use the certain mathematical...
In this paper, the signal processing by using polynomial neural network (NN) and its equivalent polynomial function is studied and simulated. To demonstrate the superiority of the equivalent polynomial function proposed, the signal recognition in a two-dimension (NC2) non-convex system and system identification were simulated and discussed. All simulations were performed by using the conventional...
This paper presents the non-stationary power signal forecasting by using a neural network with modified neurons for PJM data set provided by Independent Electricity System Operator (IESO). In this data set, the load information is the sum of power load consumed by three areas, including Allentown, Baltimore and Philadelphia. The historical load and temperature information from year 2003 to year 2008...
This paper presents the identifications of ammonia concentration by using several different neural network (NN) models. The shear horizontal surface acoustic wave (SH-SAW) device coated with polyaniline (PANI) film was applied as ammonia sensor. The data sensed by SH-SAW sensor was implemented by these NN models. A reliable and superior intelligent identifier is expected to be found for effectively...
In this paper, an AI estimator of electric contract capacity for community antenna television system (CATV) based on quantum neural network (QNN) is proposed. This intelligent estimator not only can make CATV company have a good planning on the development of TV network system and power demand, but also can greatly reduce the company's running cost. In this AI estimator, the neural model was used...
In this paper, the neural network estimator for mechanical property of rolled steel bar was proposed. Based on the learning capability of neural network, the nonlinear, complex relationships among the steel bar, the billet materials and the control parameters of production are expected to be automatically developed. Such a neural network estimator can help the technician to make a precise judgment...
The aim of this research is to predict the luminous intensity and wavelength of light-emitting diode (LED) chip by using neural network technique. The data simulated was measured by electrical luminescence (EL) technique. The well trained neural model could be used to predict the optoelectronic attributes of LED chip in advance. The predicted results are expected to help the engineer can modify the...
In this paper, a hybrid supervised learning algorithm for neural network was proposed. The problem of local minimum learning usually occurred in the real application of neural network is tried to be solved or reduced. In order to improve the efficiency and stability of conventional error back-propagation learning algorithm, a hybrid learning method combining the linear multi-regression and backpropagation...
In this paper, the non-stationary power load forecasting by using neural model with different layer structures is presented. In the neural forecasting model we developed, the neuron types used in different layers are different. Each layer is composed of the same kind of neurons. A reliable and accurate neural forecasting model for the non-stationary power loads is trying to be found in this study...
In this paper, an intelligent refrigeration ton (RT) forecaster of the ice-storage air-conditioning system by using neural network is proposed. It is expected to help the operator could precisely set the RT values needed for the air-conditioner and then reach the goal of energy saving. To demonstrate the superiority of the forecaster we developed, the same simulations by using traditional methods,...
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