Echo state networks (ESNs) have become one of the most effective recurrent neural networks because of its good prediction performance in real-valued time-series modeling tasks and simple training processes. The origin...
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ISBN:
(纸本)9781467371902
Echo state networks (ESNs) have become one of the most effective recurrent neural networks because of its good prediction performance in real-valued time-series modeling tasks and simple training processes. The original ESN concept uses fixed randomly created reservoirs, and this concept is considered of its main adv.ntages. However, ESN has been criticized for its randomly created connectivity and weight structure. Finding an optimal reservoir for a given task is an important problem. To address this problem, a cycle reservoir with regular jump network (CRJN) model was proposed. To improve the performance of CRJN and keep the output weights small, we present a simple disjunction algorithm (SDA). First, an appropriately sized reservoir is employed. The weight rates of each internal neuron are then calculated. Finally, internal neurons with weight rates exceeding a threshold are spread into two neurons. A system identification and two time-series benchmark tasks are applied to demonstrate the feasibility and superiority of SDA. Results show that the SDA method outperforms several other improved approaches. Furthermore, this method is able to keep the output weights small, thus increasing the stability of CRJN.
Aiming at difficulty modeling of large amounts of industrial process data, a novel soft sensor model based on artificial immune agent-based multiple model Radial Basis Function (RBF) networks is proposed in this paper...
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Aiming at difficulty modeling of large amounts of industrial process data, a novel soft sensor model based on artificial immune agent-based multiple model Radial Basis Function (RBF) networks is proposed in this paper. The method is to predict the qualities of manufactured products of Crude Oil Tower. In the IMMST-Team system, some biological immune based operation and learning rules which can efficiently cluster the large amounts of data samples and adapt RBF submodels structure and parameter are established. Individuals in the immune system work cooperatively to accomplish the goal of model training. Meenwhile, immune memory and pattern recognition provide high efficiency of predicting. The prediction of dry point of naphtha produced in a practical industrial process is carried out as a case study. The results obtained indicate that the proposed method provides quality prediction with high efficiency and accuracy, which is capable of learning the relationships between process variables measured during the production cycle.
作者：
BULL, DNDaniel N. Bull
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