Recognition, Based On The Letter Of The Neural Network Optimization Of Scheduling And Phase Characteristics

Artificial neural network has been used for solving optimal control and pattern recognition problems extensively. In this dissertation, we concentrate our attention to applying artificial neural network in both cell optimal scheduling in ATM environments and phase feature recognition of 3-D objects.1. Cell optimal scheduling based on neural network: Two new energy functions of Hopfieid neural network are employed based on the multiple input-queuing cooperating with the policy of more than one cell transferred in each input line during every time slot. And then we use the Hopfieid neural network to control and schedule the cells. The computer simulation results show that our approaches not only greatly improve the throughput but also lower down the cell loss probability and reduce the average latency of ATM switching fabrics. That is the performances of ATM switching fabrics are improved due to reducing the head of line blocking (HOL blocking).Considering parallel information processing, easy accomplishment by electronic or optoelectronic technique of Hopfieid neural network and the ATM Switching Fabric and the buffer without speedup, the two approaches proposed in this dissertation are effective cell scheduling schemes.2. Phase feature recognition based on neural network: A new approach based on phase features combined with neural network model is proposed for recognizing 3-D objects in this dissertation. First, the phase features of a transparent or semitransparent 3-D object were extracted by wavelength-scanning digital holography and numerical reconstruction technique. Then, A BP neural network trained by those reconstructed images including the phase features of 3-D objects was used to recognize the input objects. The computer simulations show that the correct recognition rate is up to 100% for the training objects or ones with some scale variance. It demonstrates that the method we proposed is effective. And it gives a new way for recognizing the transparent or semitransparent 3-D objects with some scale invariance.