Beginning with the seminal work of [1], the last half-decade of artificial intelligence and computer vision has been dominated by the stunning success of convolutional neural networks (CNNs). In visual recognition, a robust classifier must be able to recognize objects under deformation. One solution that has been proposed for improving invariance under rotation is complex-valued CNNs [2, 3]. Wh...

In recent years, real-valued neural networks have demonstrated promising, and often striking, results across a broad range of domains. This has driven surge applications utilizing high-dimensional datasets. While many techniques exist to alleviate issues high-dimensionality, they all induce cost in terms network size or computational runtime. work examines the use quaternions, form hypercomplex...

the proposed iafc neural networks have both stability and plasticity because theyuse a control structure similar to that of the art-1(adaptive resonance theory) neural network.the unsupervised iafc neural network is the unsupervised neural network which uses the fuzzyleaky learning rule. this fuzzy leaky learning rule controls the updating amounts by fuzzymembership values. the supervised iafc ...

PREFACE Artificial neural networks or simply neural networks represent an emerging technology rooted in many disciplines. This popular and important area of science and technology was extensively developing for the recent period of time. Neural networks are endowed with some unique attributes, like the ability to learn from and adapt to their environment and the ability to approximate very comp...

Recently models of neural networks that can directly deal with complex numbers, complex-valued neural networks, have been proposed and several studies on their abilities of information processing have been done. In this paper, the problem of amplitude estimation of sinusoidal signals from observations corrupted by colored noise using Hopfield neural network (HNN) is considered. We have introduc...

We propose a way of using boolean circuits to perform real valued computation in a way that naturally extends their boolean functionality. The functionality of multiple fan in threshold gates in this model is shown to mimic that of a hardware implementation of continuous Neural Networks. A Vapnik-Chervonenkis dimension and sample size analysis for the systems is performed giving best known samp...

We propose a way of using boolean circuits to perform real valued computation in a way that naturally extends their boolean func-tionality. The functionality of multiple fan in threshold gates in this model is shown to mimic that of a hardware implementation of continuous Neural Networks. A Vapnik-Chervonenkis dimension and sample size analysis for the system is performed giving best known samp...

Gradient-based parameter optimization is commonly used for training neural networks and optimizing the performance of other complex systems that only contain continuously differentiable functions. However, there is a large class of important parameter optimization problems involving systems containing discretevalued functions that do not permit the direct use of gradient-based methods. Examples...