A novel method for Constructive Neural Networks has been proposed to detect the skin cancer stages by using C-MANTEC algorithm. This algorithm produces compact architecture. To analyze the skin cancer at the early stage adaptive region growing is proposed. For the execution of skin malignancy identification there are four phases included. First stage is pre-processing by anisotropic diffusion filter, second stage is segmentation, and third stage is GLCM feature extraction by using MATLAB. Utilizing constructive neural network algorithm in fourth stage is used to detect various stages of the cancer which is further implemented using Xilinx.

Through the research on the existing C-MANTEC neural network and PID control technology, this paper presents an improved C-MANTEC algorithm based on PID control system. The combining of the artificial neural networks with conventional PID control helps in exploring their respective advantages to forming the intelligent PID control. From UCI Repository cancer dataset, the developed system is tested. The results show that the scheme can not only improve the speed of the algorithm in the training process but also improve the generalization capability of the network, which further enhances the performance of PID controllers. The overall power consumed is also reduced to a greater extent.

A simple and robust real time controller that works very well for linear systems with optimal gain tunings is the PID controller. But, PID controllers do not work properly if plant dynamics are changing fast or when the plant is highly nonlinear. However, many of the industries still rely on it. Hence, in most of the plants an auxiliary controller coexists to help the primary PID controller to work better by compensating for uncertainties present during control operation.Neural network has proven to be a good candidate as this auxiliary nonlinear controller. Neural network can effectively compensate for unknown uncertainties and also act as an intelligent control. The success of the neural network as an auxiliary controller has been reported in practical applications such as motion control system, signal processing and controlling robot manipulators. Nowadays, parallel programmable logic devices, such as the field programmable gate array (FPGA), have become powerful hardware options, offering low cost, high execution speed, reconfigurability and parallelism. This work intends to exploit the current available resources in commercial FPGAs to implement servo control for hard disk drive system. Simulation and experimental results included in this paper show the viability of exploiting the parallelism and modularity of a Virtex 6 FPGA to implement a high sampling rate neural network-RBF based controller. This control system platform will allow fast prototyping of new control concepts and evaluation of non-linear control.