The operational life of rotating machines has to be extended using a predictive condition maintenance tool. Among various condition monitoring techniques, vibration analysis is most widely used technique in industry. Signals are extracted for evaluating the condition of machine; further diagnostics is carried out with detected signals to extend the life of machine. With help of detected signals...

Three different recurrent neural network (RNN) architectures are studied for the prediction of geomagnetic activity. The RNNs Elman, gated unit (GRU), and long short-term memory (LSTM). take solar wind data as inputs to predict Dst index. index summarizes complex processes into a single time series. models trained tested using five-fold cross-validation based on hourly resolution OMNI dataset f...

This paper presents a set of techniques that allow generating a class of testbeds that can be used to test recurrent neural networks’ capabilities of integrating information in time. In particular, the testbeds allow evaluating the capability of such models, and possibly other architectures and algorithms, of (a) categorizing different time series, (b) anticipating future signal levels on the b...

This paper proposes two hybrid connectionist structural acoustical models for robust context independent phone like and word like units for speaker-independent recognition system. Such structure combines strength of Hidden Markov Models (HMM) in modeling stochastic sequences and the non-linear classification capability of Artificial Neural Networks (ANN). Two kinds of Neural Networks (NN) are i...

Hidden units in multi-layer networks form a representation space in which each region can be identified with a class of equivalent outputs (Elman, 1989) or a logical state in a finite state machine (Cleeremans, Servan-Schreiber & McClelland, 1989; Giles, Sun, Chen, Lee, & Chen, 1990). We extend the analysis of the spatial structure of hidden unit space to a combinatorial task, based on binding ...

A criticism of neural network architectures is their susceptibility to “catastrophic interference” the ability to forget previously learned data when presented with new patterns. To avoid this, neural network architectures have been developed which specifically provide the network with a memory, either through the use of a context unit, which can store patterns for later recall, or which combin...

Cooperative coevolution decomposes a problem into subcomponents and employs evolutionary algorithms for solving them. Cooperative coevolution has been effective for evolving neural networks. Different problem decomposition methods in cooperative coevolution determine how a neural network is decomposed and encoded which affects its performance. The problem decomposition method should provide eno...