We present an application of multi-objective evolutionary optimization of feed-forward neural networks (NN) to two real world problems, car and face classification. The possibly conflicting requirements on the NN are speed and classification accuracy, both of which can enhance the embedding systems as a whole. We compare the results to the outcome of a greedy optimization heuristic (magnitude-b...

Integration of Global Positioning System (GPS) and Inertial Navigation System (INS) has been extensively used in aircraft applications like autopilot, to provide better navigation, even in the absence of GPS. Even though Kalman Filter (KF) based GPS/INS integration provides a robust solution to navigation, it requires prior knowledge of the error model of INS, which increases the complexity of ...

Knowledge-Based Neural Networks (KBNN) are concerned with the use of domain knowledge to determine the initial structure of Neural Networks(NN). KBNN are shown to classify better unseen examples than randomly initialized NN. In this paper we study the potential of Case-Based Reasoning (CBR) for further improvement of a trained KBNN. The idea is to apply CBR only for correction of KBNN solutions...

The slow convergence and local minima problems associated with neural networks (NN) used for non-linear system identification have been resolved by evolutionary techniques such as differential evolution (DE) combined with Levenberg Marquardt (LM) algorithm. In this work the authors attempted further to employ an opposition based learning in DE, known as opposition based differential evolution (...

Generation of natural-sounding synthetic speech from a text requires perfect control over the temporal structure of speech flow. The present paper describes an attempt to replace the rule-based durational model, hitherto used in Estonian text-tospeech synthesis, by neural networks (NN). For this aim, fluent speech of radio announcers and newsreaders was analysed and its temporal structure was m...

Neural networks are significantly used in signal and image processing techniques for pattern recognition and template matching. In this work neural networks are used for image compression. In order to improve the performances image compression algorithm, DWT is combined with NN for achieving better MSE and increase in compression ration greater than 100%. NN architecture achieves maximum of 98%...

In conventional neural networks (NN) based parametric text-tospeech (TTS) synthesis frameworks, text analysis and acoustic modeling are typically processed separately, leading to some limitations. On one hand, much significant human expertise is normally required in text analysis, which presents a laborious task for researchers; on the other hand, training of the NN-based acoustic models still ...

Transient stability evaluation (TSE) is part of dynamic security assessment of power systems, which involves the evaluation of the system’s ability to remain in equilibrium under credible contingencies. Neural networks (NN) have been applied to the security assessment of power systems and have shown great potential for predicting the security of power systems. This paper proposes a generalized ...

System identification in nonstationary environment represents a challenging problem and an advaned neural architecture namely Time-Varying Neural Networks (TV-NN) has shown remarkable identification properties in nonlinear and nonstationary conditions. Timevarying weights, each being a linear combination of a certain set of basis functions, are used in such kind of networks instead of stable on...

In recent years, Long Short-Term Memory Recurrent Neural Networks (LSTM-RNNs) trained with the Connectionist Temporal Classification (CTC) objective won many international handwriting recognition evaluations. The CTC algorithm is based on a forward-backward procedure, avoiding the need of a segmentation of the input before training. The network outputs are characters labels, and a special non-c...