Ensemble methods for classification and regression have focused a great deal of attention in recent years. They have shown, both theoretically and empirically, that they are able to perform substantially better than single models in a wide range of tasks. We have adapted an ensemble method to the problem of predicting future values of time series using recurrent neural networks (RNNs) as base l...

In this paper an extension to a regular recurrent neural network (ERNN) is presented. It allows to train the ERNN without the limitation of using input information just up to a preset future frame. It is possible to train the ERNN simultanously in positive and negative time direction, leading in regression and classi cation experiments to results better than merging the outputs of separate netw...

There are two basic methods for performing steepest descent in fully recurrent networks with n noninput units and m = O(n) input units. Backpropagation through time (BPTT) [e.g., Williams and Peng (1990)l requires potentially unlimited storage in proportion to the length of the longest training sequence but needs only O(n’) computations per time step. BPTT is the method of choice if training se...

While Truncated Back-Propagation through Time (BPTT) is the most popular approach to training Recurrent Neural Networks (RNNs), it suffers from being inherently sequential (making parallelization difficult) and from truncating gradient flow between distant time-steps. We investigate whether Target Propagation (TPROP) style approaches can address these shortcomings. Unfortunately, extensive expe...

Presence, the impression of being existent in artificially created places, is an important factor for achieving maximum experience in virtual environments (VEs) and hence in their use for experiments or therapy. To date, most clinical studies have used selfreport questionnaires or physiological measures to appraise the degree of presence. Some studies that have tested behavioral scales have use...

In the paper we present a new approach based on application of neural networks to detect SQL attacks. SQL attacks are those attacks that take advantage of using SQL statements to be performed. The problem of detection of this class of attacks is transformed to time series prediction problem. SQL queries are used as a source of events in a protected environment. To differentiate between normal S...

We introduce an extended Backpropagation Through Time (eBPTT) learning algorithm for SegmentedMemory Recurrent Neural Networks. The algorithm was compared to an extension of the Real-Time Recurrent Learning algorithm (eRTRL) for these kind of networks. Using the information latching problem as benchmark task, the algorithms’ ability to cope with the learning of long-term dependencies was tested...

Wet, dry, and ash weights and calcium content were determined on demarrowed tibiae of normal and 'hypophysectomized' chick embryos from days 105 to 19-5 of incubation. All parameters noted above increased progressively through developmental time in normal embryos. VHypophysectomy' significantly affected bone wet and dry weights, and total mineral and calcium content. The data indicate that mine...

Modeling of water vapor transport through a dry soil layer (DSL), typically formed in the top during seasons arid and semi-arid areas, is still problematic. Previous laboratory experiments controlled environments showed that only process DSL by Fick's law diffusion. However, field exhibited consistently higher evaporation rates than predicted diffusion flow only. Some proposed reasons for misma...