A technique for pulse code modulation (PCM) encoding using a T-Model neural network is described. Performance evaluation on both the T-Model and the Hopfield model neural-based PCM encoders is carried out with PSpice simulations. The PSpice simulations also show that the T-Model neural-based PCM encoder computes to a global minimum much more effectively and more quickly than the Hopfield one.

We report on an experimental hysteresis in the Hopfield networks and examine the effect of the hysteresis on some important characteristics of the Hopfield networks. The detail mathematic description of the hysteresis phenomenon in the Hopfield networks is given. It suggests that the hysteresis results from fully–connected interconnection of the Hopfield networks and the hysteresis tends to makes the Hopfield networks difficult to reach the global minimum. This paper presents a T–Model network approach to overcoming the hysteresis phenomenon by employing a half–connected interconnection. As a result, there is no hysteresis phenomenon found in the T–Model networks. Theoretical analysis of the T–Model networks is also given. The hysteresis phenomenon in the Hopfield and the T–Model networks is illustrated through experiments and simulations. The experiments agree with the theoretical analysis very well.