paper presents the advances of a research using a combination of recurrent and feed-forward neural networks for long term prediction of chaotic time series. It is known that point-to-point, long term prediction for chaotic time series is not possible; however, in this research we are looking for ways to build dynamical systems using artificial neural network, that contain the same characteristi...

In Japan, cramming of too much knowledge into students was criticized and more relaxed education policy has been introduced to develop the individuality of each student. If students can afford to study carefully, creativity of the individual is fostered. We consider that it is very important to pay attention to “Affordable” concept in the field of engineering. In our previous research, we have ...

Chaotic attractors of discrete-time neural networks include innnitely many unstable periodic orbits, which can be stabilized by small parameter changes in a feedback control. Here we explore the control of unstable periodic orbits in a chaotic neural network with only two neurons. Analytically a local control algorithm is derived on the basis of least squares minimization of the future deviatio...

Cooperative coevolution decomposes a problem into subcomponents and employs evolutionary algorithms for solving them. Cooperative coevolution has been effective for evolving neural networks. Different problem decomposition methods in cooperative coevolution determine how a neural network is decomposed and encoded which affects its performance. The problem decomposition method should provide eno...

Chaotic attractors of discrete-time neural networks include infinitely many unstable periodic orbits, which can be stabilized by small parameter changes in a feedback control. Here we explore the control of unstable periodic orbits in a chaotic neural network with only two neurons. Analytically, a local control algorithm is derived on the basis of least squares minimization of the future deviat...

This paper serves as a tutorial on the use of neural networks for solving combinatorial optimization problems. It reviews the two main classes of neural network models: the gradientbased neural networks such as the Hopfield network, and the deformable template approaches such as the elastic net method and self-organizing maps. In each class, the original model is presented, its limitations disc...