Logical radial basis function networks a hybrid intelligent model for function approximation

Hefny, Hesham A.; Bahnasawi, Ahmed A.; Wahab, Ashraf H. Abdel; Shaheen, Samir I.

doi:10.1016/s0965-9978(98)00124-0

Cited by 10 publications

(3 citation statements)

References 9 publications

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“…The major advantages of ANNs (Artificial Neural Networks) were their abilities to adapt (or learn), to generalize and to make highly nonlinear mapping among different domains. The highly parallel processing nature of the ANNs provided them also with the fault tolerance properties [12]. So, used neural networks to predict future water demand would have a great advantage.…”

Section: The Evaluation Of the Forecast Resultsmentioning

confidence: 99%

The Research on Forecasting Water Demand Methods for Changzhi City in Shanxi Province

Liu

2009

2009 First International Conference on Information Science and Engineering

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With China's social and economic rapid development, the conflict between the water supply and demand had become increasingly conspicuous, and would have the further deterioration tendency. Therefore, to forecast water demand was very important for the water resources reasonable plan and the optimized allocation. There were a variety of methods to predict the water demand; however, for different cities or regions, it was necessary to choose the right forecasting method to predict the water demand. Take Changzhi city in Shanxi Province as the modeling object, and then use the gray model GM(1,1) and the RBF (radial basis function) neural network to predict the water demand of Changzhi city in 1998、1999 and 2000 years. Two prediction models have high prediction accuracy. The maximum relative error of the gray model GM (1, 1) was 8.16%, the average absolute relative error was 4.08%; the maximum relative error of the RBF neural network model was 6.89%, the average absolute relative error was 2.74%. The results shown that the prediction accuracy of the RBF neural network model was better than the gray model GM (1, 1), and the forecast for years was longer than the gray model GM (1, 1), so the RBF neural network was more suitable to forecast water demand for Changzhi city.

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Section: The Evaluation Of the Forecast Resultsmentioning

confidence: 99%

The Research on Forecasting Water Demand Methods for Changzhi City in Shanxi Province

Liu

2009

2009 First International Conference on Information Science and Engineering

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“…Thus, the model is more suitable for the forecast of the annual water demand in Yangquan and the forecast cycle is longer than the GM (1,1) model. The neural network has self-organization and self-learning capacity and can produce nonlinear mapping among different dimensions, having the fault-tolerant capacity [6][7][8] . Therefore, the forecast of the future water demand through the neural network is predominant.…”

Section: Discussionmentioning

confidence: 99%

Application of the grey theory and the neural network in water demand forecast

Liu

Ming-qi

2010

2010 Sixth International Conference on Natural Computation

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With the rapid development of society and economy of China, the imbalance between supply and demand is becoming increasingly conspicuous and toward further worsening. Therefore, the forecast of water demand is rather important for the reasonable planning and optimum distribution of water resources. There are various forecast methods for water demand. For a different city or region, a different and proper forecast method should be selected for the forecast. We take Yangquan City of Shanxi as the modeling object and respectively adopt grey forecast GM (1,1) model and RBF neural network model to forecast the water demand of Yangquan City in 1998, 1999 and 2000. For the grey forecast GM (1,1), the maximum relative error is -19.50%, the mean relative error is -13.85%.For the RBF neural network model, the maximum relative error is 2.29% and the mean relative error is 2.01%. The result indicates that the forecast precision of the RBF neural network model is better than that of the grey forecast GM (1,1) model and the forecast period is longer than that of the grey forecast GM (1,1) model. If both compared, the RBF neural network is more applicable for the forecast of the water demand of Yangquan City.

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“…It is very much efficient when it is made to work in parallel with other modeling techniques. The combination of all three intelligent concepts will yield maximum favorable outcome and vastly accepted as a hybrid methodologies [119]. A learning technique at par with the neural network is developed by [120] which is relied on the genetic fuzzy combination.…”

Section: Hybrid Techniquesmentioning

confidence: 99%

Recent Advances in Intelligent-Based Structural Health Monitoring of Civil Structures

Paul¹,

Jafari²

2018

Adv. sci. technol. eng. syst. j.

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This survey paper deals with the structural health monitoring systems on the basis of methodologies involving intelligent techniques. The intelligent techniques are the most popular tools for damage identification in terms of high accuracy, reliable nature and the involvement of low cost. In this critical survey, a thorough analysis of various intelligent techniques is carried out considering the cases involved in civil structures. The importance and utilization of various intelligent tools to be mention as the concept of fuzzy logic, the technique of genetic algorithm, the methodology of neural network techniques, as well as the approaches of hybrid methods for the monitoring of the structural health of civil structures are illustrated in a sequential manner.

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Logical radial basis function networks a hybrid intelligent model for function approximation

Cited by 10 publications

References 9 publications

The Research on Forecasting Water Demand Methods for Changzhi City in Shanxi Province

The Research on Forecasting Water Demand Methods for Changzhi City in Shanxi Province

Application of the grey theory and the neural network in water demand forecast

Recent Advances in Intelligent-Based Structural Health Monitoring of Civil Structures

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