Open Access
Wave Height Forecasting Using Cascade Correlation Neural Network
Hamidreza Rashidy Kanan,Karim Faez +1 more
- 01 Jan 2004
- pp 77-80
TL;DR: A cascade correlation neural network is used for prediction of wave heights at given times due to the useful capability of this network for prediction and appr oximation and various simulations show that the cascade correlation network has better performance with α=0.005.
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Abstract: Forecasting of wave height is necessary in a large numbe r of ocean coastal activities. Recently, neural networks are used for prediction and approximation of wave height s in sea and ocean due to their great convergence rate. In this paper a cascade correlation neural network is used for prediction of wave heights at given times due to the useful capability of this network for prediction and appr oximation. Results of different prediction for 500 data points in cascade correlation neural network are compared with those of the M.L.P. (Multi-layer Perceptron) neural network. These results show that cascade correla tion network has larger convergence rate compared with M.L.P. network. Also various simulations show that the cascade correlation network has better performance with α=0.005 (Learning-rate), sigmoid activation function for hidden units and linear activation function for output units.
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Citations
Day Ahead Ocean Swell Forecasting With Recursively Regularized Recurrent Neural Networks
D.T. Mirikitani
- 18 Jun 2007
TL;DR: In this paper, a recurrent multilayer perceptron neural network (RMLPNN) was proposed to predict ocean swell amplitude at fixed deep water observation platforms, which can provide critical decision-making information for a large number of costal ocean activities.
2
Artificial Neural Network Model for Analysis Ultimit Bearing Capacity of Single Pile
Batas Tiang Tunggal,Niken Silmi Surjandari,A. Aziz Djajaputra,Sri Prabandiyani +3 more
- 01 Sep 2010
TL;DR: The main objective of this research is to develop neural network model to predict the ultimit bearing capacity of single pile and the neural network predictions were found to be more reliable than other.
1
•Dissertation
Artificial Intelligence Techniques in Reservoir Characterization
Ahmed A. Adeniran
- 07 Feb 2009
TL;DR: This work investigated the suitability of some of the recently proposed advances in neural networks technique including, functional networks (FN), cascaded correlation neural networks, polynomial networks, and general regression neural networks for predicting porosity and water saturation from well logs.
References
Artificial neural network applications in geotechnical engineering
Mohamed A. Shahin,Mark B. Jaksa,Holger R. Maier +2 more
- 01 Jan 2001
TL;DR: A review of the literature reveals that ANNs have been used successfully in pile capacity prediction, modelling soil behaviour, site characterisation, earth retaining structures, settlement of structures, slope stability, design of tunnels and underground openings, liquefaction, soil permeability and hydraulic conductivity, soil compaction and soil swelling and classification of soils.
361
Real time wave forecasting using neural networks
Makarand Deo,C. Sridhar Naidu +1 more
TL;DR: This paper presents a complementary and simple method to make a point forecast of waves in real time sense based on the current observation of waves at a site that incorporates the technique of neural networks.
346
Connectivity and performance tradeoffs in the cascade correlation learning architecture
Dhananjay S. Phatak,Israel Koren +1 more
TL;DR: The cascade correlation algorithm is modified to generate networks with restricted fan-in and small depth by controlling the connectivity and the results reveal that there is a tradeoff between connectivity and other performance attributes like depth, total number of independent parameters, and learning time.
Fast initialization for cascade-correlation learning
TL;DR: Empirical simulations show that the new method can significantly speed-up the cascade-correlation learning compared to the case where the candidate training is used, and the overall performance remained similar or was even better than with the candidateTraining.
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Function evaluation and the cascade-correlation architecture
TL;DR: The cascade-correlation architecture which is designed for classification tasks has been studied to see if it can be modified to perform function evaluation and interpolation tasks.
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