Representing diverse mathematical problems using neural networks in hybrid intelligent systems

Li, Hong Xing; Li, L.X

doi:10.1111/1468-0394.00118

Cited by 19 publications

(3 citation statements)

References 7 publications

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“…The artificial neural network has the potential to represent complex, nonlinear relationships [25], [26], [27], [28], [29], [30], [31], [32], [33], [34]. The evolution of ANN has given rise to the multilayer perceptron (deep learning), which is effective at modelling and predicting complex, nonlinear relationships in time series.…”

Section: Ann Modelmentioning

confidence: 99%

Intelligent Hybrid ARIMA-NARNET Time Series Model to Forecast Coconut Price

et al. 2023

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The global demand for coconut and coconut-based products has increased rapidly over the past decades. Coconut price continues to fluctuate; thus, it is not easy to make predictions. Good price modelling is important to accurately predict the future coconut price. Several studies have been conducted to predict the price of coconut using various models. One of the most important and widely used models in time series forecasting is the autoregressive integrated moving average (ARIMA). However, price fluctuations is considered a problem with uncertain behaviour. The existing ARIMA time series model is unsuitable for solving this problem, because of the nonlinear series. Artificial neural networks (ANN) have been an effective method in solving nonlinear data pattern problems in the last two decades. The non-linear autoregressive neural network (NARNET) gives good forecast, most especially when series are non-linear. Therefore ARIMA-NARNET is considered a universal approach to forecasting the coconut price. The aim of the study is to establish a linear and nonlinear model in time series to forecast coconut prices. The ability of a hybrid approach that combines ARIMA and NARNET(ANN) models is investigated. Based on the experimental study, the experimental results show that the proposed method ARIMA-NARNET, is better at forecasting the price of coconut, an agriculture commodity, than both the ARIMA model and NARNET models. The expected benefit of the proposed forecasting model is it can help farmers, exporters, and the government to maximize profits in the future.INDEX TERMS ARIMA-NARNET, intelligent hybrid, coconut price, forecasting, time series.

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Section: Ann Modelmentioning

confidence: 99%

Intelligent Hybrid ARIMA-NARNET Time Series Model to Forecast Coconut Price

et al. 2023

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“…The deep neural network is the most used field of AI systems and the basis of many modern AI applications (LeCun et al, 2015). The performance of deep neural networks in speech recognition (Deng et al, 2013) and image recognition (Krizhevsky et al, 2012) makes it widely used in industrial, social and life scenarios (Lu et al, 2012; Panigrahi et al, 2019; Xing Li & Li, 1999; Zhou et al, 2003; Zhu et al, 2008). Although deep neural networks show good accuracy in many AI scenarios, it comes at the cost of high computational complexity.…”

Section: Systems Thinking and Perspective On Ai Systemmentioning

confidence: 99%

“…The genetic algorithm also has shortcomings, such as poor local searchability, convergence performance of the algorithm and long durations to find the optimal solution. Therefore, the application of the genetic algorithm is to improve the searchability of the genetic algorithm, increase the convergence speed and finally solve the actual problem in the solution (Hong & Li, 1999; Jin et al, 2014; F. Li et al, 2011a, 2011b; Wang et al, 2011).…”

Section: Systems Thinking and Perspective On Ai Systemmentioning

confidence: 99%

Applications and development of artificial intelligence system from the perspective of system science: A bibliometric review

et al. 2022

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With the rapid development of big data, 6G, Internet of Things (IoT) and other information and communication technologies (ICTs), artificial intelligence (AI) has ushered in new opportunities for more advancements. AI systems are changing the survival and development of human society. This study focuses on the research and application of AI systems within the scientific framework of systems thinking. From scientometrics, this study adopts cocitation analysis and keywords co‐occurrence analysis to sort existing applications of AI system to research network and research overview while giving the current research hotspots of AI system application research. Concurrently, the development trend and direction of AI systems are analysed, and the opportunities and challenges faced are put forward, which lays a foundation for system research in the field of AI systems.

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An ARIMA‐ANN Hybrid Model for Time Series Forecasting

Wang

Haofei

Su³

et al. 2013

Syst. Res.

122

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Autoregressive integrated moving average (ARIMA) model has been successfully applied as a popular linear model for economic time series forecasting. In addition, during the recent years, artificial neural networks (ANNs) have been used to capture the complex economic relationships with a variety of patterns as they serve as a powerful and flexible computational tool. However, most of these studies have been characterized by mixed results in terms of the effectiveness of the ANNs model compared with the ARIMA model. In this paper, we propose a hybrid model, which is distinctive in integrating the advantages of ARIMA and ANNs in modeling the linear and nonlinear behaviors in the data set. The hybrid model was tested on three sets of actual data, namely, the Wolf's sunspot data, the Canadian lynx data and the IBM stock price data. Our computational experience indicates the effectiveness of the new combinatorial model in obtaining more accurate forecasting as compared to existing models.

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Representing diverse mathematical problems using neural networks in hybrid intelligent systems

Cited by 19 publications

References 7 publications

Intelligent Hybrid ARIMA-NARNET Time Series Model to Forecast Coconut Price

Intelligent Hybrid ARIMA-NARNET Time Series Model to Forecast Coconut Price

Applications and development of artificial intelligence system from the perspective of system science: A bibliometric review

An ARIMA‐ANN Hybrid Model for Time Series Forecasting

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