artificial neural networks have the advantages such as learning, adaptation, fault-tolerance, parallelism and generalization. this paper mainly intends to offer a novel method for finding a solution of a fuzzy equation that supposedly has a real solution. for this scope, we applied an architecture of fuzzy neural networks such that the corresponding connection weights are real numbers. the sugg...

Patients turn to Online Health Communities not only for information on specific conditions but also for emotional support. Previous research has indicated that the progression of emotional status can be studied through the linguistic patterns of an individual’s posts. We analyze a realworld dataset from the Mental Health section of healthboards.com. Estimated from the word usages in their posts...

In this paper we develop the gamma neural model, a new neural net architecture for processing of temporal patterns. Time varying patterns are normally segmented into a sequence of static patterns that are successively presented to a neural net. In the approach presented here segmentation is avoided. Only current signal values are presented to the neural net, that adapts its own internal memory ...

Since the initial introduction of Open Source Cluster Application Resources (OSCAR), this software package has been a well-accepted choice for building high performance computing systems. As it continues to be applied to mission-critical environments, high availability (HA) features therefore are needed to be included in OSCAR cluster. In this paper, we provide a HA solution for OSCAR cluster. ...

ARTIFICIAL BRAINS AN EVOLVED NEURAL NET MODULE APPROACH PDF Are you looking for ARTIFICIAL BRAINS AN EVOLVED NEURAL NET MODULE APPROACH Books files? Now, you will be happy that at this time ARTIFICIAL BRAINS AN EVOLVED NEURAL NET MODULE APPROACH PDF is available at our online library. With our complete resources, you could find ARTIFICIAL BRAINS AN EVOLVED NEURAL NET MODULE APPROACH PDF or just...

This paper presents a continuous-time recurrent neural-network model for nonlinear optimization with any continuously differentiable objective function and bound constraints. Quadratic optimization with bound constraints is a special problem which can be solved by the recurrent neural network. The proposed recurrent neural network has the following characteristics. 1) It is regular in the sense...

What are neural nets? The biological model: The human brain The components of a neural net What they can and where they fail The learning process What means "learning" refering to neural nets? Supervised and unsupervised learning Forwardpropagation Backpropagation Selforganization