A neural network architecture for detecting moving objects. II

For pt.I see Proc. of the 3rd Italian Workshop of Parallel Architectures and Neural Networks. Summary form only given. In pt.I the author proposed an architecture for solving a problem of processing time-varying inputs. In that architecture, the signal is processed in a spatio-temporal dimension. Time is not the independent variable in the solution of a set of differential equations as in the classical case, but it plays an essential role in the interaction on the time-varying input and its processing. The purpose of the net is not, as usually, to classify and/or recognize patterns, nor to solve a problem of minimum energy, but to detect some characteristics of a signal varying with respect both to time and space. Such a network has been proved useful in solving the problem of detecting moving objects in a cluster. In this part, the architecture of the net is outlined and its performance is discussed together with its similarities and differences with respect to cellular neural networks. Results of computer simulations are given and the problem of hardware implementation is considered