runoff estimation is one of the main challenges encountered in water and watershed management. spatial and temporal changes of factors which influence runoff due to het-erogeneity of the basins explain the complicacy of relations. artificial neural network (ann) is one of the intelligence techniques which is flexible and doesn’t call for any much physically complex processes. these networks can...

This report provides detailed description and necessary derivations for the BackPropagation Through Time (BPTT) algorithm. BPTT is often used to learn recurrent neural networks (RNN). Contrary to feed-forward neural networks, the RNN is characterized by the ability of encoding longer past information, thus very suitable for sequential models. The BPTT extends the ordinary BP algorithm to suit t...

an adaptive input-output linearization method for general nonlinear systems is developed without using states of the system. another key feature of this structure is the fact that, it does not need model of the system. in this scheme, neurolinearizer has few weights, so it is practical in adaptive situations.  online training of neurolinearizer is compared to model predictive recurrent training...

We consider the problem of training input-output recurrent neural networks (RNN) for sequence labeling tasks. We propose a novel spectral approach for learning the network parameters. It is based on decomposition of the cross-moment tensor between the output and a non-linear transformation of the input, based on score functions. We guarantee consistent learning with polynomial sample and comput...

Hisashi Iwade, ∗ Kohei Nakajima, 3 Takuma Tanaka, and Toshio Aoyagi Graduate School of Informatics, Kyoto University, Yoshida Honmachi, Sakyo-ku, Kyoto 606-8501, Japan Graduate School of Information Science and Technology, University of Tokyo, Tokyo 113-8656, Japan JST, PRESTO, 4-1-8 Honcho, Kawaguchi, Saitama 332-0012, Japan Faculty of Data Science, Shiga University, 1-1-1 Banba, Hikone, Shiga...

Frequently, sequences of state transitions are triggered by specific signals. Learning these triggered sequences with recurrent neural networks implies storing them as different attractors of the recurrent hidden layer dynamics. A challenging test and also useful for application is conditional prediction of sequences giving just the trigger signal as an input and letting the recurrent neural ne...

We introduce a novel regularization approach for a class of inputdriven recurrent neural networks. The regularization of network parameters is constrained to reimplement a previously recorded state trajectory. We derive a closed-form solution for network regularization and show that the method is capable of reimplementing harvested dynamics. We investigate important properties of the method and...

We introduce a novel constraint optimization approach for a class of input-driven recurrent neural networks. A unified network model allows algebraic derivation of optimal network parameters using the Lagrange multiplier method. Regularization of weights serves as optimality criterion, while the solution is constraint to implement given network state dynamics. We derive the analytical, closed f...

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