By analyzing the problem of job performance interference in distributed machine learning， it is found that performance interference is caused by the uneven allocation of GPU resources such as memory overload and bandwidth competition， and to this end， a mechanism for quickly predicting performance interference between jobs was designed and implemented， which can adaptively predict the degree of job interference according to the given GPU parameters and job types. First， the GPU parameters and interference rates during the operation of distributed machine learning jobs were obtained through experiments， and the influences of various parameters on performance interference were analyzed. Second， some GPU parameter-interference rate models were established by using multiple prediction technologies to analyze the job interference rate errors. Finally， an adaptive job interference rate prediction algorithm was proposed to automatically select the prediction model with the smallest error for a given equipment environment and job set to predict the job interference rates quickly and accurately. By selecting five commonly used neural network tasks， experiments were designed on two GPU devices and the results were analyzed. The results show that the proposed Adaptive Interference Prediction （AIP） mechanism can quickly complete the selection of prediction model and the performance interference prediction without providing any pre-assumed information， it has comsumption time less than 300 s and achieves prediction error rate in the range of 2% to 13%， which can be applied to scenarios such as job scheduling and load balancing.

A low complexity method was proposed for the blind recognition of BCH codes under error conditions. The existing recognition methods most come from the generic methods of linear block codes, which can't be applied when the code length is long and the bit error rate is high. This method is based on that the BCH codes come from the sub-space of Hamming codes, so the parity check matrix of the hamming codes can be used to check the BCH codes. The method contains recovering the code length, synchronization and generator polynomial. The simulations show that the algorithm runs successfully for a BCH code with length 1023, when the bit error rate is 0.5%.

Based on the approximation theory of Fourier-series working in square integrable space, a Fourier neuronal network was constructed by using activation functions of the complex exponential form. Then a weightsdirectdetermination method was derived to decide the neuralnetwork weights immediately, which remedied the weaknesses of conventional BP neural networks such as small convergence rate, easily converging to local minimum and possibly lengthy or oscillatory learning process. A hidden-neurons-growing algorithm was presented to adjust the neural-network structure adaptively. Theoretical analysis and simulation results substantiate further that the presented Fourier neural network and algorithm could have good properties of high-precision learning, noise-suppressing and discontinuous-function approximating.