in this paper we present a method related to extracting white blood cells (wbcs) from blood microscopic figures and recognizing them and counting each kind of wbcs. in this method, first we extract the white blood cells from other blood cells by rgb color system's help. in continuance, by using the features of each kind of globules and their color scheme, we extract a normalized feature vector,...

INTrODUCTION Recurrent neural networks whose neurons are fully interconnected have been utilized to implement associative memories and solve optimization problems. These networks are regarded as nonlinear dynamical feedback systems. Stability properties of this class of dynamical networks are an important issue from applications point of view. ABSTrACT Global stability analysis for complex-valu...

Context: Recently, the singular points of neural networks have attracted attention from the artificial intelligence community, and their interesting properties have been demonstrated. The objective of this study is to provide an overview of studies on the singularities of complex-valued neural networks. Evidence Acquisition: This review is based on the relevant literature on complex-valued neur...

Recurrent correlation neural networks (RCNNs), introduced by Chiueh and Goodman as an improved version of the bipolar correlation-based Hopfield network, can be used to implement high-capacity associative memories. In this paper, we extend RCNNs for processing hypercomplex-valued data. Precisely, present mathematical background a broad class RCNNs. Then, address stability new using synchronous ...

Title Type control of flexible link manipulators using neural networks PDF control of flexible link manipulators using neural networks 1st edition PDF control of robot manipulators in joint space advanced textbooks in control and signal processing PDF constructive neural networks PDF digital neural networks PDF complex valued neural networks PDF control of redundant robot manipulators theory an...

Here, a new idea to speed up the operation of complex valued time delay neural networks is presented. The whole data are collected together in a long vector and then tested as a one input pattern. The proposed fast complex valued time delay neural networks uses cross correlation in the frequency domain between the tested data and the input weights of neural networks. It is proved mathematically...

The complex valued neural networks are the extended version of conventional real valued neural networks Input and output signals weighting factors and neuron nonlinear functions are determined using complex number so that the information geometry of the network is constructed in complex space This feature is advantageously used especially for learning and expressing smooth dynamical attractors ...

In this paper, the natural gradient descent method for the multilayer stochastic complex-valued neural networks is considered, and the natural gradient is given for a single stochastic complex-valued neuron as an example. Since the space of the learnable parameters of stochastic complex-valued neural networks is not the Euclidean space but a curved manifold, the complex-valued natural gradient ...

A complex valued neural network is a neural network, which consists of complex valued input and/or weights and/or thresh olds and/or activation functions. Complex-valued neural networks have been widening the scope of applications not only in electronics and informatics, but also in social systems. One of the most important applications of the complex valued neural network is in image and visio...