Introduction: This study aimed to present a system based on intelligent models that can enhance the accuracy of diagnostic systems for acute leukemia. The three parts including preprocessing, feature extraction, and classification network are considered as associated series of actions. Therefore, any dysfunction or poor accuracy in each part might lead in general dysfunction of...

Adaptive networks are a novel class of dynamical networks whose topologies and states coevolve. Many real-world complex systems can be modeled as adaptive networks, including social networks, transportation networks, neural networks and biological networks. In this paper, we introduce fundamental concepts and unique properties of adaptive networks through a brief, non-comprehensive review of re...

the stability of learning rate in neural network identifiers and controllers is one of the challenging issues which attracts great interest from researchers of neural networks. this paper suggests adaptive gradient descent algorithm with stable learning laws for modified dynamic neural network (mdnn) and studies the stability of this algorithm. also, stable learning algorithm for parameters of ...

Adaptive networks are a novel class of dynamical networks whose topologies and states coevolve over similar time scales. Many real-world complex systems (physical, biological and social ones) can be modeled as adaptive networks. In this short paper, we introduce fundamental concepts and unique properties of adaptive networks through a brief, noncomprehensive review of recent literature. We also...

Various artificial neural networks types are examined and compared for the prediction of surface roughness in manufacturing technology. The aim of the study is to evaluate different kinds of neural networks and observe their performance and applicability on the same problem. More specifically, feed-forward artificial neural networks are trained with three different back propagation algorithms, ...