In information and knowledge economy era, the competition among enterprises has been extended in space and strengthened in time. In order to obtain long-term survival and sustainable development, the enterprises must pay more attention to their employees.
Therefore, enterprises need to construct a whole set of comprehensive and objective overall performance evaluation system.
Employee is the masses foundation and impetus for organization development and expansion, through understanding and evaluating employee correctly, the enterprise not only can impel employee effectively, but also can guarantee the organization rapid and sustainable development.
There are many successful applications of Backpropagation (BP) for training multilayer neural networks. Performance Evaluation is one of the application of Backpropagation. However, it has many shortcomings. Learning often takes long time to converge, and it may fall into local minima. One of the possible remedies to escape from local minima is by using a very small learning rate, which slows down the learning process.
We propose to implement the above application i.e Employee Performance Evaluation using Radial Basis Neural networks containing radial basis functions can be used in many of the same situations in which back-propagation networks are used.