We present Dynamic Sampling Convolutional Neural Networks (DSCNN), where the position-specific kernels learn from not only the current position but also multiple sampled neighbour regions. During sampling, residual learning is introduced to ease training and an attention mechanism is applied to fuse features from different samples. And the kernels are further factorized to reduce parameters. Th...

As computers become more and more powerful, as algorithms improve, more and more computing power is behind the engineering systems’ behavior. As a result of this increasing sophisticated data processing and decision making, many current systems exhibit more and more intelligent behavior. However, no matter how intelligent they may seem, in comparison with the previous less sophisticated systems...

Because of their parallelism, functional approXimation and learning capabilities, artificial neural networks can be effectively employed to apjmximate nonlinear functions, and to synthesize controllers for nonlinear dynamic systems. The use of dynamic neural networks to model and control dynamic systems is of great importance in the control paradigm. The intent of this paper is to use one such ...

in this paper, a novel hybrid method based on learning algorithmof fuzzy neural network and newton-cotesmethods with positive coefficient for the solution of linear fredholm integro-differential equation of the second kindwith fuzzy initial value is presented. here neural network isconsidered as a part of large field called neural computing orsoft computing. we propose alearning algorithm from ...