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Aiming at solving the problems that the CHNN-RIHM (Complex-valued Hopfield Neural Network Real-Imaginary-type Hard-Multistate-activation-function) is easy to get into a local optimization and requires a large amount of data, we propose to use Signal Blind Detection Algorithm Based on MC-CHNN (Multi-Clustering Complex-valued Hopfield Neural Network), construct a new energy function and prove the stability of MC-CHNN. The optimization scheme of MC-CHNN is as follows: we use a piecewise annealing function to improve the convergence speed of blind detection. To further reduce the complexity of the MC-CHNN algorithm and improve the sensitivity of MC-CHNN, we propose to apply a new activation function called the multi-clustering function to MC-CHNN and replace the activation function of CHNN-RIHM with multi-clustering function when dealing with discrete multilevel signals. The simulation results show that the MC-CHNN has a faster convergence speed, stronger anti-noise capability, and can be better applied to low SNR than its competitors.

Aiming at the blind detection of the air quality monitoring system, based on cluster processing of Wireless Sensor Network, combined with WSN blind detection internal model in literature, this paper proposes self-regulating mutation differential evolution to restore signals of reference sensor, then uses newly-constructed model to experiment on the system. Simulation demonstrates that compared with traditional difference blind equalization algorithm, BER of improved algorithm is obviously reduced while all internal sensor data can be restored under acceptable condition.

Combining the theory of correlation and simple ant colony optimization blind algorithms, basing on the transmission cluster partition model of wireless sensor networks, we propose a new blind detection scheme. The scheme first uses blind detection algorithm based on simple ant colony optimization algorithms to derive the reference node information and in turn, to estimate the transmission channel. It then combines the anti-estimated transmission channel of reference sensor nodes to recover other non-reference nodes information in the clusters according to the correlation among the transmission signal of each sensor in the model. Simulation results show the feasibility of the scheme.

Based on panel data, the paper utilized knowledge production function model to study the knowledge spillover effects of universities RD The technology absorbing ability of enterprises could significantly affect the knowledge spillover effects of FDI and universities R&D on the LMSIE technology innovation.

The paper has utilized spatial econometrics knowledge production function models based on panel data to study the influence of the institutions of higher education knowledge spillovers on new products development of the large & medium-sized industrial enterprises (LMSIE). The study has indicated that: (i)The pool was first-order stationary sequence and regression series was heterogeneous cointegration relations. Moran’s I index have shown that the new products development of LMSIE has the feature of spatial correlation. (ii) The contribution of institutions of higher education to new products development of LMSIE relies on the R & D expenditure of universities and the scale of university-enterprise cooperation. (iii)The new products development in the neighboring areas would play the negative functions of “replacement” and “expulsion”.