Digital Image Recognition Based on Improved Cognitive Neural Network

Abstract This paper presents an innovative cognitive neural network method application in digital image recognition. The following conclusion can be drawn. Each point of the graph is transformed, and the original color of the transformed new coordinates is given to the point. If after all the points have transformed, if there is a point and no point has converted to this point, the point is not given a color. Then this point will form a hole or a stripe, and the color is the color of the point initialization. The innovative method can effectively separate the digital image recognition signal from the mixed signal and maintain the waveform of the source signal with high accuracy, thus laying the foundation for the next step of recognition.


Introduction
In recent years, many scholars have conducted many discussions on the topic of digital image recognition and have proposed many methods. This recognition method has a good effect in terms of recognition rate and reliability [1]. In the literature, the clustering technology and genetic algorithm have combined, and a method integration method based on similarity propagation algorithm and genetic algorithm has applied to handwritten digit recognition.
In the literature, the characteristics of wavelet transform a in image processing has proposed a new offline handwritten digital feature extraction of local Fourier transform.
In this paper, digital image recognition system has designed based on RBF method.
An innovative algorithm is an adaptive learning rate of adjustment algorithm, so that the learning rate of the network weight and threshold adjustment process can be based on the network error surface. The curvature of the different regions changes adaptively.
In this paper, the digital pictures used for training and testing have read and binarized to form a corresponding digital matrix [2].
Then the matrix used for training has brought into the method for weights and threshold.
The test result of the innovative RBF method has a higher recognition rate in digital image recognition.

Cognitive neural network
The learning process of the cognitive neural network method is different from the BP way. The hidden layer in the cognitive neural network is a sigmoid function whose value is a non-zero value in an infinitely large range in the input space [4]. The cognitive neural network method is a global approximation network with three-layer forward network.
According to the empirical formula, the stepby-step test method determines the number of hidden layer nodes, and gradually calculates the input vector and the corresponding center distance, and then gradually increases the value based on this value. By comparing the entire RBF network can predict the network performance, choose the best performance [5,6]. The number of nodes corresponding to the entire RBF network can be used as the number of hidden layer neuron nodes.
Network output: The network output connection weights form a connection matrix.
Hidden node number I, center point: The relationship between network input and output: The performance index function of the cognitive neural network approximation is as follows:  Step 1: Start with the number of the hidden neurons by 1.
Step 2: Perform k-means clustering to find the location of centers for the hidden numbers.
Step 3: Select the width of each neuron to be half of the maximum distance between the center itself and other neurons.
Step 4: Using pseudo-inverse method to obtain the weight.
Step 5: For a selected Q value, compute the current methods error bound by the following equation.
Step 6: Find the minimum error bound, and output the corresponding hidden neuron's number.

Method Construction and Training
The method used for digital image recognition in this paper has one input layer, each output layer has 16 nodes, the number of nodes is 1.
Among them, the excitation of the first output layer is sigmoid, and the excitation of the second one is an S-type tangent function [8].
The global error is set to 01001. 300 training samples are input into the cognitive neural network method for training. The innovative cognitive neural network method in digital image recognition are shown in the follow (see Figure 4).

Method performance test
The 100 test samples have input into the trained method, and the comparison between the recorded output results and the target results showed that the correct rate was 92% [9]. The innovative cognitive neural network method has a high recognition rate in image recognition.

The original image
The original image by improved RBF Figure 6. Changes in cognitive neural network during the recovery period The training results are shown in Figure 6.
For the given experimental lattice image, all the dots in the last line are white pixels, so that after the transformation, a white stripe is added at the bottom of the pattern than the result of the reference experiment report [10]. In Fig. 6

Discussions
The potential application prospects in the areas of office automation, automatic meter

Conclusions
With the continuous development of social