Neural networks are an algorithmic model that mimics the structure of the human brain. The principle lies in distributed information storage and parallel collaborative processing. Although the function of each unit is very simple, a network system composed of a large number of units can realize very complicated data calculation and is also a highly complex nonlinear dynamic learning system.
The structure of the neural network is closer to the human brain, with massive parallelism, distributed storage and processing, self-organization, self-adaptation and self-learning capabilities. The use of neural networks is very extensive, and it can be used in the fields of system identification, pattern recognition, and intelligent control. Nowadays, the most attractive IT giants are concerned with the automatic learning function of the neural network in the field of intelligent control. It is especially suitable for dealing with related problems, such as voice, when it is necessary to substitute certain conditions and the information itself is uncertain and fuzzy. Identification.
Principle of neural network algorithmSince the design of the neural network algorithm is too large, we will only analyze the principle of the Microsoft neural network algorithm for the time being. In the Microsoft neural network algorithm, we can simplify it into the following picture:
The "multilayer perceptron" network consisting of up to three layers of neurons used by the Microsoft neural network are: the input layer, the optional hidden layer, and the output layer.
Input layer: Input neurons define all input attribute values ​​and probabilities of the data mining model.
Implicit layer: A hidden neuron accepts input from an input neuron and provides an output to the output neuron. A hidden layer is a location that assigns weights to various input probabilities. Weights indicate the relevance or importance of a particular sacred to hidden neurons. The greater the weight assigned to the input, the more important the input value is. And this process can be described as the process of learning. The weight can be negative, indicating input suppression rather than promoting a particular outcome. Output layer: The output neurons represent the predictable attribute values ​​of the data mining model.
Data is passed from the input to the intermediate hidden layer to the output. The whole process is a process of propagating data and information from front to back. The data value on the next layer of nodes is transmitted from the previous node connected to it, and then the data is weighted. After a certain function operation, a new value is obtained, and the propagation proceeds to the next layer node. This process is a forward propagation process.
When the node output error occurs, that is, different from the expectation, the neural network will automatically "learn", and the latter layer node has a "trust" degree to the previous layer node (in fact, the weight of the connector is changed), and the reduction is taken. The weighting method is used to punish. If the node output is thick and thick, then it is necessary to check the impact of those input nodes, reduce the weight of the node connection that caused the error, punish these nodes, and improve the connections of the nodes that make the correct suggestions. the weight of. For those nodes that are punished, the same method is used to punish the node before it until the input node. This is called: feedback. The process of our study is to repeat the process described above, get the input value through forward propagation, and learn by feedback method. When all the data in the training set has been run through, it is called a training cycle. After training, the neural network model is obtained, which includes the corresponding values ​​in the training set and the laws that influence the changes in the predicted values.
There are complex functions in the hidden layers in each neuron, and these are nonlinear functions, and are similar to the basic transmission characteristics of biological neural networks. These functions are called activation functions, ie, the input values ​​are subtle. Changes sometimes have large output changes.
Application of neural network algorithmBased on the research of network models and algorithms, artificial neural networks are used to form practical application systems, for example, to perform certain signal processing or pattern recognition functions, to construct expert systems, to make robots, to control complex systems, and so on.
Throughout the history of the development of emerging science and technology, human beings have experienced a bumpy road in the process of conquering space, basic particles, and the origin of life. We will also see that the study of human brain function and neural networks will be constantly changing with the difficulty of overcoming difficulties.
Although neural networks are now widely used in the field of speech recognition, their use is certainly not limited to this. Next, neural networks are most likely to enter the field of image software. Similar to the process of resolving sounds, when the neural network analyzes an image, each layer of image detectors first looks for features in the image, such as the edges of the image.
When the probe is complete, another layer of software combines the edges to form features such as corners of the image. Then repeating this way, the identified image features will become clearer and clearer. By the end of the last layer, all the image features will be combined and compared with the data in the database to find out what the objects in the image are. in conclusion.
The aforementioned Google Dean research team used this method to develop a set of software that can be used to identify cats in online video. Perhaps the future of this software will be extended to the field of image search, Google Street View can use this algorithm to distinguish the characteristics of different things. In addition, neural networks have room to expand in the medical field. A research team at the University of Toronto has successfully used neural networks to analyze the possible ways in which drug molecules can function in the real world.
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