This paper deals with a new approach to detect the structure (i.e. determination of the number of hidden units) of a feedforward neural network (FNN). This approach is based on the principle that any FNN could be represented by a Volterra series such as a nonlinear inputoutput model. The new proposed algorithm is based on the following three steps: first, we develop the nonlinear activation fun...

This paper describes the development of neural model-based control strategies for the optimisation of an industrial aluminium substrate disk grinding process. The grindstone removal rate varies considerably over a stone life and is a highly nonlinear function of process variables. Using historical grindstone performance data, a NARX-based neural network model is developed. This model is then us...

Abstract This paper demonstrates the Gaussian process regression model's applicability combined with a nonlinear autoregressive exogenous (NARX) framework using experimental data measured PZTs' patches bonded in composite aeronautical structure for concerning novel SHM strategy. A stiffened carbon-epoxy plate regarding healthy condition and simulated damage on center of bottom part stiffener is...

Bitcoin is a decentralized digital currency that enables people to exchange value without requiring third-party intermediary. Due its many advantages, it has received much interest from institutional and individual investors. Despite meteoric increase, the price of extremely volatile asset class as purely relies on supply demand. This presents an interesting opportunity create forecasting model...

The identification of switched systems is a challenging problem, which entails both combinatorial (sample-mode assignment) and continuous (parameter estimation) features. A general framework for this problem has been recently developed, alternates between parameter estimation sample-mode assignment, solving tasks to global optimality under mild conditions. This article extends the nonlinear cas...

The quantification of wave loading on offshore structures and components is a crucial element in the assessment their useful remaining life. In many applications well-known Morison's equation employed to estimate forcing from waves with assumed particle velocities accelerations. This paper develops grey-box modelling approach improve predictions force structural members. A model intends exploit...

The northern Gulf of Mexico coast is affected by the North Atlantic hurricane season, which causes storm surge disasters every year and brings serious economic losses to southern USA; therefore, it necessary make an accurate advance prediction level. In this paper, a model with simple structure, fast computation speed, results has been constructed based on nonlinear auto-regressive exogenous (N...

Multimedia services became a major part of the internet network traffic. The bursty characteristics of the video traffic, produced by applications like video on demand, video broadcasting or videoconferencing, make it difficult to fulfill the Quality of Service (QoS) of the multimedia applications. Therefore it is important to utilize congestion control procedures. One of the procedures used to...

This paper compares the performance of Nonlinear Autoregressive Exogenous (NARX) Neural Network and Support Vector Machine (SVM) regression model to predict Air Pollutant Index (API) in Malaysia. Two models namely NARX SVM were developed using API air quality time series data from three monitoring stations: Pasir Gudang, TTDI Jaya Larkin. Hourly parameters collected year 2016 2018 utilized prod...

This paper presents a new algorithm for identification of NARX Hammerstein systems using support vector machines (SVMs) to model the static nonlinear elements. The SVM is fitted by minimizing an ε-insensitive, L-1 cost function which is robust in the presence of outliers. Another advantage of this algorithm is that the value of the uncertainty level epsilon can be specified by the user which gi...