Artificial neural network simulator with integrated learning supervision

Michaud, François; Rubio, Ruben Gonzalez; Dalle, D.; Ward, Steve

doi:10.1109/cjece.1995.7102061

Cited by 2 publications

(2 citation statements)

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“…The flood location analyzed was Kelang river located at Petaling bridge, Kuala Lumpur. This RBFNN model used [4,5, I] structure whereas the Improved RBFNN used [1,7,1] structure. The structures were obtained using trial and error method.…”

Section: Introductionmentioning

confidence: 99%

“…An important factor that influenced the widely used ANN model in flood prediction was the input-output relationship of the ANN model. The learning stage in the ANN model was in a form of supervised learning thus developing systematic relationship between input-output data in ANN model [4,5]. Based on "generalized delta rule" [6], supervised learning gradually modify the weights and biases value in the ANN model to reduce the error between the predicted and the actual output.…”

Section: Introductionmentioning

confidence: 99%

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Modeling of flood water level prediction using improved RBFNN structure

Adnan

Samad

Tajjudin

et al. 2015

2015 IEEE International Conference on Control System, Computing and Engineering (ICCSCE)

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Recently, the applications of Artificial Neural Network (ANN) in various hydrologic problems have becoming popular. This is due to ability of ANN models to estimate nonlinear functions and hence become important tools to solve diverse water resources problems. Particularly, ANN models have been used in hydrological fields such as river flow forecasting, rainfall-runoff estimation, flood prediction and water quality prediction. Therefore, this paper proposed flood water level prediction model using Radial Basis Function Neural Network (RBFNN) and Improved RBFNN structure that using the water level data from Kelang river which is located at Jambatan Petaling, Kuala Lumpur. The models were developed by processing offline data over time using neural network architecture. The methodologies and techniques of the two models were presented in this paper and comparison of the long term runoff time prediction results between them were also conducted. The prediction results of the Radial Basis Function Neural Network architecture indicate fair performance for the one hour ahead of time prediction. The performance indices results also concluded that the Improved RBFNN model was more reliable than that of the original RBFNN model.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling of flood water level prediction using improved RBFNN structure

Adnan

Samad

Tajjudin

et al. 2015

2015 IEEE International Conference on Control System, Computing and Engineering (ICCSCE)

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Autonomous Design of Artificial Neural Networks by Neurex

Michaud

Rubio

1996

Neural Computation

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Artificial neural networks (ANN) have been demonstrated to be increasingly more useful for complex problems difficult to solve with conventional methods. With their learning abilities, they avoid having to develop a mathematical model or acquiring the appropriate knowledge to solve a task. The difficulty now lies in the ANN design process. A lot of choices must be made to design an ANN, and there are no available design rules to make these choices directly for a particular problem. Therefore, the design for an ANN demands a certain number of iterations, mainly guided by the expertise and the intuition of the developer. To automate the ANN design process, we have developed Neurex, composed of an expert system and an ANN simulator. Neurex autonomously guides the iterative ANN design process. Its structure tries to reproduce the design steps done by a human expert in conceiving an ANN. As a whole, the Neurex structure serves as a framework to implement this expertise for different learning paradigms. This article presents the system's general characteristics and its use in designing ANN using the standard backpropagation learning law.

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Artificial neural network simulator with integrated learning supervision

Cited by 2 publications

References 0 publications

Modeling of flood water level prediction using improved RBFNN structure

Modeling of flood water level prediction using improved RBFNN structure

Autonomous Design of Artificial Neural Networks by Neurex

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