This paper presents the potential of the herein so-called neural net filters in communication systems. The use of integrated signaling and coding techniques in modern modulation schemes has not a right correspondence in the same digital signal processing integrated tools. The neural net filter seems to be the needed contribution. Introduced from de basic neural structure associated with coding ...

This paper describes ongoing ATR's CAM-Brain Project, which is an attempt to build large-scale neural networks ('artiicial brains') in a special hardware called "CAM-Brain Machine" (CBM). At the time of writing (March 1998), the project is making eeorts on two fronts-the construction of the CBM, that is scheduled to be operational in the summer of 1998, and attempting to nd an eecient and eeect...

This paper introduces TiPo, a new neural net model with superior evolvabilities than all previously known neural net models for dynamic functions. TiPo neural nets can dynamically change their structure with each clock tick. This provides enhanced computability for highly dynamic functions, such as curve following. Our goal is to implement this model in a secondgeneration brain-building machine...

The neighborhood effect is a key driving factor for the land-use change (LUC) process. This study applies convolutional neural networks (CNN) to capture neighborhood characteristics from satellite images and to enhance the performance of LUC modeling. We develop a hybrid CNN model (conv-net) to predict the LU transition probability by combining satellite images and geographical features. A spat...

Introduction What is an artificial neural network and how does it work? Artificial neural network have been developed from generalizations of neural biology model, based on the assumptions that (a) information processing occurs at many simple elements called neurons, (b) signals are passed between neurons over connection links, (c) each connection link has an associated weight, which, in a typi...

In order to track a maneuvering target, multiple model (MM) methods have been researched. Almost MM algorithms have been developed based on Markov process. However, Markov based MM method is difficult to design and application-dependent. To solve this problem, Daebum Choi, et al proposed basic idea of neural-net based VSMM [5]. In this paper, we will show the design procedure of neural-net base...

a novel neuro-based method is introduced to solve the laminar boundary layer and the turbulent free jet equations. the proposed method is based on cellular neural networks, cnns, which are recently applied widely to solve partial differential equations. the effectiveness of the method is illustrated through some examples.

Although recurrent neural nets have been moderately successful in learning to emulate nite-state machines (FSMs), the continuous internal state dynamics of a neural net are not well matched to the discrete behavior of an FSM. We describe an architecture, called DOLCE, that allows discrete states to evolve in a net as learning progresses. dolce consists of a standard recurrent neural net trained...