precipitation is one of important parameters of climatology and atmospheric science that have more importance in human life. recently, extensive flood and drought entered many damage to most parts of the world. precipitation forecasting and alerts management role is responsible for these problems. today, artificial neural networks are one of developed method that applied for estimate and predic...

We study the adaptation of convolutional neural networks to the complex-valued temporal radio signal domain. We compare the efficacy of radio modulation classification using naively learned features against using expert feature based methods which are widely used today and e show significant performance improvements. We show that blind temporal learning on large and densely encoded time series ...

The online gradient method has been widely used in training neural networks. We consider in this paper an online split-complex gradient algorithm for complex-valued neural networks. We choose an adaptive learning rate during the training procedure. Under certain conditions, by firstly showing the monotonicity of the error function, it is proved that the gradient of the error function tends to z...

This paper investigates the application of the multilayer perceptron structure to the packet-wise adaptive decision feedback equalization of a Mary QAM signal through a TDMA indoor radio channel in the presence of intersymbol interference (ISI) and additive Gaussian noise. Since the multilayer neural networks are capable of producing complex decision regions with arbitrarily nonlinear boundarie...

Complex-valued neural networks (CVNNs) are a powerful modeling tool for domains where data can be naturally interpreted in terms of complex numbers. However, several analytical properties of the complex domain (e.g., holomorphicity) make the design of CVNNs a more challenging task than their real counterpart. In this paper, we consider the problem of flexible activation functions (AFs) in the c...

in this paper, the application of neural networks for simulation and optimization of the cogeneration systems has been presented. cgam problem, a benchmark in cogeneration systems, is chosen as a casestudy. thermodynamic model includes precise modeling of the whole plant. for simulation of the steadysate behavior, the static neural network is applied. then using dynamic neural network, plant is...

Abstract In this paper, we first consider the stability problem for a class of stochastic quaternion-valued neural networks with time-varying delays. Next, cannot explicitly decompose systems into equivalent real-valued systems; by using Lyapunov functional and analysis techniques, can obtain sufficient conditions mean-square exponential input-to-state networks. Our results are completely new. ...