Feedback Process Neural Networks

Abstract

A feedback neural network is an artificial neural network model that has been widely applied to signal processing [1], optimal computation [2], convex nonlinear programming[3], seismic data filtering[4], etc. A traditional feedback neural network model generally has time-invariant inputs. However, when a biological neural organization processes information, it actually feeds back time-delay information and the inputs of external signals will last for a period. Its current outputs depend not only on current inputs, but also on the accumulation of all previous inputs, i.e. a temporal accumulation effect. In practical problems, many systems also have feedback control items, e.g. in a real-time process control system, the inputs of control variables usually need adjusting according to the current output quantity of the system; in some multi-objective optimization problems, the system needs to adjust dynamically to the search strategy according to current states. The feedback process neural network is just a process neural network model with information feedback, and all its neuron nodes are connected according to the information flow direction of the system. The information can be passed back to nodes in each previous layer by certain rules, and output information can be fed back to the nodes themselves. There are many forms of the feedback process neural network model. In this chapter, we mainly introduce a three-layer network model and its learning algorithm, then analyze the stability of the model. In addition, several other forms of feedback process neural network model will be given. When the feedback process neural network transports information, there are forward flows as in a feedforward neural network and time-delay feedback information that is from the latter layer nodes to the former layer nodes.