WiMi Hologram Cloud Develops Neural Network-Based Data Fusion Algorithm System to Boost Processing Capacity

WiMi Hologram Cloud a leading global Hologram Augmented Reality (“AR”) Technology provider, announced the development of neural network-based data fusion algorithm system. Data fusion is the integrated processing and optimization of multi-dimensional information acquisition, representation, and intrinsic linkages to produce complete, accurate, timely, and effective integrated information.

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With powerful self-learning, adaptive, non-linear matching, and information processing capabilities, neural networks are algorithms that imitate human brains for information processing. Applying neural network technology to data fusion can reduce redundant data transmission and improve the system’s speed, accuracy, and performance.

Neural networks usually consist of an input layer, a hidden layer, and an output layer. The multi-layer network architecture makes the output of information more accurate. The neural network algorithm is a supervised learning algorithm whose main idea is to learn from known network intrusion samples by using gradient search techniques, with the ultimate goal of minimizing the mean square error between the actual output value of the network and the desired output value. In addition, neural networks provide non-linear transfer functions and parallel processing capabilities to help perform image fusion. The neural network consists of processing nodes (neurons) connected. A neural network data fusion model is built to assign neurons and interconnect weights based on the relationship between the input and output of multi-sensor data.

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Neural networks have robust characteristics such as fault tolerance and self-learning, self-organizing and self-adaptive capabilities. The system’s classification criteria are determined based on the similarity of the samples accepted. The weight distribution of the network characterizes the process. Specific neural network algorithms are also used to acquire knowledge, obtain uncertainty inference mechanisms, and utilize neural networks’ signal processing capabilities and automatic inference functions to achieve multi-sensor data fusion.

Firstly, the system chooses its topology according to the requirements and the form of sensor information fusion. Secondly, the input information of each sensor is integrated and processed by the system 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 network’s structure through the interaction between the neural network and the environment. Finally, the system learns and understands sensor output information, determines the assignment of weights, completes the fusion of knowledge acquisition information, interprets patterns, and converts the input data vectors into high-level logical concepts.

WiMi’s system utilizes the generalization ability of neural networks and pattern recognition. It can deal with uncertain information as classifiers, fuse the sensor information obtained by the network, get the parameters of the corresponding network, convert the knowledge rules into digital form, and establish a data knowledge base. The system can acquire knowledge by extracting external information and parallel associative reasoning. The complex relationships of the uncertain environment are fused into accurate signals that the system can understand after learning and reasoning. Neural networks have the capability of massively parallel processing of information, which can enhance the speed of information processing in the data fusion algorithm system, effectively reduce redundant data transmission, increase the accuracy of data fusion, and improve the performance of the data fusion algorithm. At the same time, the distributed information storage and parallel processing features of the neural network are used to achieve real-time recognition and improve the performance of the data recognition system.

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