In the present paper a neural network approach called “Adaptive Critic Design” (ACD) was applied to optimal tuning of set point controllers of the three main substrates (sugar, nitrogen source and dissolved oxygen) for PHB production process. For approximation of the critic and the controllers a special kind of recurrent neural networks called Echo state networks (ESN) were used. Their structur...

Pneumatic muscle (PM) has many advantages such as light weight, high power to weight ratio and low price. However, it has strong time varying characteristic. The complex nonlinear dynamics of PM system poses some challenges for achieving accurate modeling and control. To solve these problems, we propose nonlinear internal model control (IMC) using echo state network (ESN) for PM system in this ...

The number of users and their network utilization will enumerate the traffic of the network. The accurate and timely estimation of network traffic is increasingly becoming important in achieving guaranteed Quality of Service (QoS) in a wireless network. The better QoS can be maintained in the network by admission control, inter or intra network handovers by knowing the network traffic in advanc...

The study of learning models for direct processing complex data structures has gained an increasing interest within the Machine Learning (ML) community during the last decades. In this concern, efficiency, effectiveness and adaptivity of the ML models on large classes of data structures represent challenging and open research issues. The paradigm under consideration is Reservoir Computing (RC),...

The prediction of stock price return volatilities is important for financial companies and investors to help measure managing market risk support decision-making. literature points out alternative models - such as the widely used heterogeneous autoregressive (HAR) specification which attempt forecast realized accurately. However, recent variants artificial neural networks, echo state network (E...

In a wireless network environment accurate and timely estimation or prediction of network traffic has gained much importance in the recent past. The network applications use traffic prediction results to maintain its performance by adopting its behaviors. Network Service provider will use the prediction values in ensuring the better Quality of Service(QoS) to the network users by admission cont...

In this paper we present our experiments with an echo state network (ESN) for the task of classifying high-level human activities from video data. ESNs are recurrent neural networks which are biologically plausible, fast to train and they perform well in processing arbitrary sequential data. We focus on the integration of body motion with the information on objects manipulated during the activi...

OBJECTIVE The Enriched Semantic Network (ESN) was introduced as an extension of the Unified Medical Language System (UMLS) Semantic Network (SN). Its multiple subsumption configuration and concomitant multiple inheritance make the ESN's relationship structures and semantic type assignments different from those of the SN. A technique for deriving the relationship structures of the ESN's semantic...

Prediction techniques have the challenge of guaranteeing large horizons for chaotic time series. For instance, this paper shows that majority can predict one step ahead with relatively low root-mean-square error (RMSE) and Symmetric Mean Absolute Percentage Error (SMAPE). However, some based on neural networks more steps similar RMSE SMAPE values. In manner, work provides a summary prediction t...