Multi-step Predictions Based on TD-DBP ELMAN Neural Network for Wave Compensating Platform
نویسنده
چکیده
The gradient descent momentum and adaptive learning rate TD-DBP algorithm can improve the training speed and stability of Elman network effectively. BP algorithm is the typical supervised learning algorithm, so neural network cannot be trained on-line by it. For this reason, a new algorithm (TDDBP), which was composed of temporal difference (TD) method and dynamic BP algorithm (DBP), was proposed to overcome the restriction. TD-DBP algorithm can make Elman network train on-line incrementally. Using the collected real time data, the modified TD-DBP algorithm was able to realize direct multi-step predictions for vertical displacement of wave compensating platform.
منابع مشابه
Traffic Signal Prediction Using Elman Neural Network and Particle Swarm Optimization
Prediction of traffic is very crucial for its management. Because of human involvement in the generation of this phenomenon, traffic signal is normally accompanied by noise and high levels of non-stationarity. Therefore, traffic signal prediction as one of the important subjects of study has attracted researchers’ interests. In this study, a combinatorial approach is proposed for traffic signal...
متن کاملOn the Sunspot Time Series Prediction Using Jordon Elman Artificial Neural Network (ann)
In this paper, multi step ahead prediction of monthly sunspot real time series are carried out. This series is highly chaotic in nature [7]. This paper compares performance of proposed Jordan Elman Neural Network with TLRNN (Time lag recurrent neural network), and RNN (Recurrent neural network) for multi-step ahead (1, 6, 12, 18, 24) predictions. It is seen that the proposed neural network mode...
متن کاملA New Version of Elman Neural Networks for Dynamic Systems Modeling
Elman network is a class of recurrent neural networks used for function approximation. It has a set of global sigmoid functions at its hidden units. That means that if the operating conditions of a process be identified, are changed the function approximation property of the network is degraded. This is due to the fact that the universes of discourse of the network is covered by global sigmoid ...
متن کاملMulti-Step-Ahead Prediction of Stock Price Using a New Architecture of Neural Networks
Modelling and forecasting Stock market is a challenging task for economists and engineers since it has a dynamic structure and nonlinear characteristic. This nonlinearity affects the efficiency of the price characteristics. Using an Artificial Neural Network (ANN) is a proper way to model this nonlinearity and it has been used successfully in one-step-ahead and multi-step-ahead prediction of di...
متن کاملApplication of Chaotic Genetic Algorithm and Elman to Assess Flight Per- formance Under Multiple Physiological Signals
In the recent years, the assessment and forecasting of flight performance based on pilot’s multiple physiological parameters has become an important theme of research. However, traditional forecasting and assessment of flight performance is mainly based on the manual assessment or explicit mathematical models, and rarely take the physiological parameters into consideration. Based on the complex...
متن کامل