Application perspectives for the Convolutional Downward Spiral Architecture

Adaptive learning is an important neural network characteristic; this means that they learn how to take care of difficult tasks by learning through illustrative samples of the problem to solve. Since neural networks can learn to tell the difference among many patterns by samples and training, there is no need to elaborate an a priori model, neither to develop specific probability distribution functions. This work presents the application results of a new architecture based on convolutional neural networks, named Convolutional Downward Spiral Architecture (CDSA), that generates digital filters automatically, which can be applied in a wide range of inspection systems. Key–Words: Convolutional networks, digital filters, artificial vision, inspection processes