A performance evaluation system of finance transportation projects is mainly researched, in which the sub-module of the highway projects evaluation, waterway projects evaluation, Passenger stations projects evaluation, Energy saving projects evaluation are incorporated. In addition, the expert knowledge are inserted in the system, the multi-layer neural network and fuzzy-set theory are used to implement Performance Evaluation system of Finance invest Transportation Projects, and the feasibility and effectiveness of the evaluation system are finally verified by practice.
Under the global economy, enterprises in the financial industry are facing plenty of opportunities and severe challenges. Aimed at providing a reference enterprise performance evaluation system for related enterprises, the proposed model helps enterprises to learn and sort out their own performance evaluation system according to this structure. A prediction model of BP neural network (BPNN) based on the wireless network is studied as the performance data prediction algorithm. Firstly, the feasibility of this algorithm is analysed through prediction training. Secondly, the proposed neural network algorithm is compared with the traditional algorithm for data prediction. It turns out that this neural network prediction algorithm based on wireless communication is not only universal to the prediction data but also superior to the traditional prediction algorithm in both error gap and relative average error compared with other traditional algorithms. On this basis, the particle swarm optimization (PSO) algorithm is also used to evaluate the performance indicators of three enterprises, and accurate numerical values are obtained to express the corresponding results. Therefore, it is concluded that the subalgorithm can be applied to the enterprise performance evaluation team in the financial industry.
Engineering Financial Performance Evaluation of Wireless Network Based on Intelligent Neural Network Model
