ASC
2006
13 years 6 months ago
2006
This paper presents a new algorithm for enhancing the efficiency of simulation-based optimisation using local search and neural network metamodels. The local search strategy is ba...
LREC
2008
13 years 6 months ago
2008
The goal of this work is to introduce an architecture to automatically detect the amount of stress in the speech signal close to real time. For this an experimental setup to recor...
ICONIP
2007
13 years 6 months ago
2007
There is no consensus on measuring distances between two different neural network architectures. Two folds of methods are used for that purpose: Structural and behavioral distance ...
ESANN
2008
13 years 6 months ago
2008
In this paper, we consider the problem of implementation of neural network in the context of the level 2 trigger of HESS2 project. We propose a hardware architecture which which ta...
ANNS
2007
13 years 6 months ago
2007
The ability to store and retrieve information is critical in any type of neural network. In neural network, the memory particularly associative memory, can be defined as the one i...
AIPRF
2007
13 years 6 months ago
2007
Instinct and experience are shown to form a potent combination to achieve effective foraging in a simulated environment. A neural network capable of evolving instinct-related neur...
AIA
2007
13 years 6 months ago
2007
In this paper, a novel and effective criterion based on the estimation of the signal-to-noise-ratio figure (SNRF) is proposed to optimize the number of hidden neurons in neural ne...
FPL
2008
Springer
13 years 6 months ago
2008
Springer
FPGA devices have witnessed popularity in their use for the rapid prototyping of biological Spiking Neural Network (SNNs) applications, as they offer the key requirement of reconf...
EUROS
2008
13 years 7 months ago
2008
This paper addresses the measurement of motion expressiveness in wheeled mobile robots. A neural network based supervised learning strategy is proposed as a method to fuse informat...
ICANNGA
2007
Springer
13 years 7 months ago
2007
Springer
Neural networks are a popular technique for learning the adaptive control of non-linear plants. When applied to the complex control of android robots, however, they suffer from se...