WiMi Hologram Cloud Inc (WIMI)

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WiMi Develops Neural Network-Based Data Fusion Algorithm System.

Source
https://www.newstrail.com/wimi-develops-neural-network-based-data-fusion-algorithm-system/

December 16, 2022

With the advancement of 5G, artificial intelligence, big data, and other technologies, as well as the digitalization of economic life, people’s requirements for processing diverse and large amounts of data and information are significantly increased. The diversity and complexity of data forms and the timeliness of data information fusion processing have greatly exceeded the information processing capacity of the human brain. In this context, the data fusion algorithm technology based on a neural network has come into being.

It is understood that the WiMi Hologram Cloud, Inc. (NASDAQ:WIMI) is developing a neural network-based data fusion algorithm system. Data fusion is the comprehensive processing and optimization of the acquisition and representation of multi-source and multi-dimensional information, its intrinsic connections, and the formation of complete, accurate, timely, and effective integrated details.
Neural networks are algorithms that mimic the human brain for information processing and have powerful self-learning, adaptive, nonlinear matching, and information processing capabilities. Applying neural network technology in data fusion can effectively reduce redundant data transmission, improve the real-time performance and accuracy of data fusion, and improve the performance of data fusion algorithms.

Neural networks usually consist of an input layer, a hidden layer, and an output layer, and the multilayer network architecture makes the information output more accurate. A neural network algorithm is a supervised learning algorithm whose main idea is to learn the available network intrusion samples using a gradient search technique. The final goal is to minimize the mean square error between the actual output value and the desired output value of the network.
In addition, neural networks provide nonlinear transfer functions and parallel processing capabilities to help perform image fusion. The neural network consists of processing nodes called neurons connected to build a neural network data fusion model that assigns neurons and interconnects weights based on the relationship between multi-sensor data inputs and outputs.

Neural networks have strong characteristics such as fault tolerance and self-learning, self-organizing, and self-adaptive capabilities. In the system model of the neural network-based data fusion algorithm developed by WiMi, its classification criteria are determined based on the similarity of samples accepted by the system. The distribution of weights of the network characterizes this process. At the same time, a neural network-specific algorithm is used to acquire knowledge and obtain uncertainty inference mechanisms. The neural network’s signal processing capability and automatic inference function are used to achieve multi-sensor data fusion.

- Firstly, according to the intelligent system requirements and the form of sensor information fusion, its topology is selected;
- secondly, the input information of each sensor is synthesized and processed into an overall input function, and this function mapping is defined as the mapping function of the relevant units.
The statistical laws of the environment are reflected in the structure of the network itself through the interaction between the neural network and the environment. Finally, the sensor output information is learned, understood, and the assignment of weights is determined to complete the knowledge acquisition information fusion. Then the input patterns are interpreted, and the input data vectors are converted into high-level logical concepts.

WiMi’s neural network-based data fusion algorithm system uses the generalization ability of neural networks and the ability of pattern recognition, which can be used as a classifier to deal with uncertain information, fuse the sensor information obtained by the network, bring the parameters of the corresponding network, convert the knowledge rules into digital form, and establish a data knowledge base.
It also uses information from the external environment to achieve automatic knowledge acquisition and parallel associative reasoning. The complex relationships of the uncertain climate are fused into accurate signals that the system can understand after learning and reasoning.

The neural network can massively parallel process information, improving the speed of information processing in the data fusion algorithm system and effectively reducing redundant data transmission, increasing the accuracy of data fusion and improving the performance of the data fusion algorithm. At the same time, the distributed information storage and parallel processing characteristics of neural networks are used to achieve real-time recognition and improve the performance of data recognition systems.