Composite Artificial Neural Network Research Articles

An emergent addition for the optimal systemization of wastewater utilization plants using artificial intelligence.

The treatment of wastewater is an essential factor in preventing pollutants and promoting the quality of the water. The inherent complexity, influential impact and the solid waste infrastructure all lead to confusion and variance in the primary clarifier for wastewater. These inconsistencies lead to variations in the purity and capacity constraints of wastewater and the existential impact of water receipt. Water treatment is a complicated task that has chemical, technical and biochemical aspects. A credible artificial neural network (ANN) method is necessary for another wastewater treatment plant to prevent the breakdown of the processes. Virtual reality seems to have become a strong solution for preventing waste management uncertainties and problems. This is not only due to extreme changes but also to significant external disturbances that water systems are subjected to when controlling challenges. Climate is among the most significant of such disturbances. Various environmental conditions actually include different influx frequencies and levels of substances. Water contamination has become one of the extremely serious growing concerns; sewage treatment plant identification is a key major issue here and the agencies enforce tighter requirements when operating wastewater software systems. This article plans to create models of achievement and prospects when possible future guidance of recent research borders for the use of artificial intelligence in wastewater treatment plants that concurrently deal with pollutants. This study has shown us that the composite ANN provides a greater level of competence in plant prediction and systemization.

1A1-X01 領域分割ニューラルネットワークによる仮想ロボットの行動獲得(進化・学習とロボティクス)

This paper describes a behavior acquisition by a composite artificial neural network (CANN) and a region divided artificial neural network (RDANN). The CANN includes a high-level ANN, which is used for deciding which low-level ANN is activated to calculate output signals. The RDANN is a special case of the CANN, in which the structure or the selection function of the high-level ANN is given by a designer. To examine the relation between parameters of the high-level ANN and learning effectiveness, a CANN is applied for pole balancing problems. The controller, a CANN, a RDANN or a common ANN, is optimized by the particle swarm optimization. Experimental results show that the angle of the pole is important for selection by the high-level ANN. Results of additional experiment show that the RDANN that is designed to select low-level ANNs by the angle of pole is the most effective for pole balancing.

複合的人工ニューラルネットワーク

This paper describes a novel method of combining artificial neural networks (ANNs), the composite artificial neural network (CANN), to improve performance of evolving ANNs (EANNs) in dynamic system control problems. Methods of combining ANNs by majority voting or averaging are not effective in controlling dynamic systems by EANNs. Unsupervised learning of EANNs is mathematically described, and then it is shown that the reason for ineffectiveness depends on the mechanism of evaluating ANNs indirectly by states. To avoid this problem, the CANN selects the suitable ANN by a high-level ANN to combine some ANNs. In numerical experiments, a flapping flight model is controlled by a common EANN and the CANN. The model motion is calculated by physical engine PhysX, and a common EANN and the CANN are optimized by the particle swarm optimization (PSO) respectively. The experiments show that the average evaluation of the CANN is 6.46% higher than that of a common EANN for the same computational time.