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The application and research of artificial neural network in foundation deformation prediction, over the past decades and at home and abroad, are reviewed. Based on the two main aspects of foundation pit deformation, which are horizontal displacement of supporting structure and surface settlement around foundation, the evolution and corresponding research results of neural network prediction are reviewed and summarized when the input vector of neural network changes from historical deformation to multi-source data then to combination of them. On the basis of a thorough analysis and discussion, this paper points out the further research direction and development prospects.

This paper proposed a new Two Phase Parallel Particle Swarm Optimization(TP_PPSO) Algorithm based on region and social study of multi-object optimization in economic data analysis and mining. This study(1) changes the traditional particle information sharing mechanism and introduces region study for making that particles can reference other particles information when they updates; and divides particles groups evolution into two classes optimization by the advantage of divided-group strategy . (2) partitions particles swarm evolution into two phase. One phase is propitious to expand the scope of the search and another phase is conducive to the fine overall search. (3) proposes an disturbance strategy Which the best particles in each particles swarm will be initialized in locally around and other particles will initialized in the overall scope, when the un-updated population of global optimal solution is bigger than certain population. (4) proves the convergence of PSO and convergence condition and the rationality of TPPPSO. (5) demonstrates that of TPPPSO has improved the accuracy of the solution 60% at least than the classical parallel particles swarm algorithm -IPPSO by optimization and contrastive experiments using five functions.

Function discovery is an important research direction in data mining and economic statistical target forecast. Gene expression programming (GEP) is a new tool to discovery the function in economic target analysis field. To overcome the deficiency such as pre-maturity and biggish stagnancy generation in GEP, this study (1) Introduces a dynamic mutation operator ( DM-GEP ) and flexibility controlling of population scale (FC-GEP) for more faster jumping local optimum trap and shortening average convergence generation in evolution, (2) Proposes a genome diversity-guided of grading evolution strategy for stakeout and melioration of GEP evolution process, (3) implements a multi-genome child-population parallel genetic strategy and a PED- GEP algorithm for increasing average maximal fitness and success ratio, and (4) demonstrates the effectiveness and efficiency of the new algorithm by extensive experiments, Comparising with transitional GEP, the average convergence generation is decrease to 35 % at least, and average maximal fitness increases 8 %leastways.