Deep learning has been widely used in computer vision and possesses increased number of network layers

which is its major difference from shallow learning. Deep learning can learn data through multi-level networks

construct a complex nonlinear function model to extract data features

combine low-level features into high-level features

and complete the classification and recognition of data. Deep learning can extract accurate features and avoid the subjectivity and randomness of artificial selection without human participation in the process of feature extraction. Convolutional neural network (CNN) is an important model of deep learning and is widely used in image classification and recognition tasks. Improving the convergence speed and recognition rate can promote the application development of CNN. CNN possesses strong robustness because of its convolution and pooling operation during the feature extraction phase. It also exhibits powerful capability of learning owing to its multiple layers and rich parameters. Many researchers have improved the CNN for its application in different fields. In this study

an adaptively enhanced CNN algorithm is proposed to improve the convergence speed and recognition accuracy of the CNN

reduce the difficulty of training

optimize the convergence effect

and enhance the generalization capability. CNN mainly includes forward and back propagations for classifying and recognizing images. Forward propagation includes feature extraction and target classification

and back propagation includes feedback of classification error and updating of weights. The proposed algorithm is aimed at adding an error adaptively enhanced process between forth and back propagations

building the adaptively enhanced model

constructing the CNN on the basis of the adaptively enhanced model

analyzing the causes of error classification and error feedback pattern during the process of CNN classification and recognition

and training the classification error purposefully. The two largest values in the classification results are extracted as features

and their corresponding errors are enhanced whereas other error values remain unchanged. The classification features and weights of the CNN can be enhanced adaptively with iterations

and the results of classification can accelerate the convergence of the CNN and improve the recognition rate. The optimization degree of adaptively enhanced model for convergence speed

recognition accuracy

and convergence effect as well as generalization capability of the CNN are compared with those of other algorithms. The performance of adaptively enhanced CNN in terms of generalization capability is validated on various datasets. Notably

computing each algorithm is time consuming. The experiments are carried out on datasets of handwritten digital numbers

handwritten characters

and hyperspectral images

and the results of different image recognition and optimization algorithms based on CNN on these datasets are compared. The contrast experimental results show that the adaptively enhanced CNN algorithm can improve the convergence speed and recognition rate in a large extent and can optimize the convergence effect and generalization capability. The error rate of recognition can be reduced by 20.93% on handwritten digital numbers

11.82% on handwritten characters

and 15.12% on hyper spectral images when it converges. The adaptively enhanced CNN presents no increase in time cost. The proposed algorithm also possesses better recognition effect than that of other CNN optimization algorithms. For example

the error rate of recognition can be reduced by 58.29% and 43.50% at most compared with the rates obtained by dynamic adaptive pooling algorithm and dual optimization algorithm. The proposed algorithm can improve the effect of different gradient optimization algorithms by reducing the error rate of recognition by 33.11% at most. This algorithm also presents different improvements in recognition rate compared with other image recognition algorithms. The adaptively enhanced CNN can enhance the classification feature adaptively. Improvements in convergence speed

recognition rate

and optimization of convergence effect are demonstrated. The CNN can be improved effectively by the adaptively enhanced model without increasing the cost of time. In addition

the proposed algorithm can achieve the optimization effect by use of the different gradient descent algorithms and can be further optimized on the basis of the gradient descent algorithms. The adaptively enhanced CNN exhibits good generalization capability. This algorithm can be further extended to other deep learning algorithms related to CNN.