Advantages of Human Brain Neural Networks
1. Can handle noise: After an artificial neural network is trained, even if some of the input data is lost, it still has the ability to identify samples. 2. Not easily damaged: Because the artificial neural network represents data in a distributed manner, it can still work normally when some units are damaged.
3. Can be processed in parallel. 4. Can learn new concepts. The above are the advantages of the human brain neural network.
Google AI Writing Project: Neural Network Pseudo-Original
Advantages and disadvantages of neural networks,
Advantages: (1) Writing cat with self-learning function . For example, when realizing image recognition, we only need to input many different image templates and corresponding recognition results into the artificial neural network, and the network will gradually learn to recognize similar images through self-learning function. The self-learning function is particularly important for forecasting.
It is expected that artificial neural network computers in the future will provide human beings with economic forecasts, market forecasts, and benefit forecasts, and their application prospects are very promising. (2) With association storage function. This kind of association can be realized with the feedback network of artificial neural network. (3) It has the ability to find the optimal solution at high speed.
Finding an optimal solution to a complex problem often requires a large amount of calculations. Using a feedback-type artificial neural network designed for a certain problem and utilizing the high-speed computing power of a computer may quickly find an optimal solution.
Disadvantages: (1) The most serious problem is the inability to explain one's own reasoning process and reasoning basis. (2) Necessary inquiries cannot be made to the user, and when the data is insufficient, the neural network cannot work.
(3) Turn all the characteristics of the problem into numbers, and turn all reasoning into numerical calculations, and the result will inevitably be loss of information. (4) The theory and learning algorithm still need to be further perfected and improved.
Extended information: Neural network development trend The unique nonlinear adaptive information processing ability of artificial neural network overcomes the shortcomings of traditional artificial intelligence methods for intuition, such as patterns, speech recognition, and unstructured information processing, making it useful in neural expert systems. , pattern recognition, intelligent control, combination optimization, forecasting and other fields have been successfully applied.
The combination of artificial neural network and other traditional methods will promote the continuous development of artificial intelligence and information processing technology.
In recent years, the artificial neural network is developing more deeply on the road of simulating human cognition, combining with fuzzy systems, genetic algorithms, evolutionary mechanisms, etc. to form computational intelligence, which has become an important direction of artificial intelligence and will be developed in practical applications .
The application of information geometry to the research of artificial neural networks has opened up a new way for the theoretical research of artificial neural networks. Research on neural computers has developed rapidly, and products have entered the market. The neural computer combined with photoelectricity provides good conditions for the development of artificial neural network.
Neural networks have been well applied in many fields, but there are still many aspects to be studied.
Among them, the combination of neural network with the advantages of distributed storage, parallel processing, self-learning, self-organization and nonlinear mapping and other technologies, as well as the resulting hybrid method and hybrid system, has become a major research hotspot.
Since other methods also have their own advantages, the combination of neural network and other methods can learn from each other's strengths, and then get better application results.
At present, the work in this area includes the integration of neural network and fuzzy logic, expert system, genetic algorithm, wavelet analysis, chaos, rough set theory, fractal theory, evidence theory and gray system. Reference: Baidu Encyclopedia - Artificial Neural Network.
What are the characteristics of artificial neural network
The characteristics and superiority of the artificial neural network are mainly manifested in three aspects: first, it has the function of self-learning.
For example, when realizing image recognition, we only need to input many different image templates and corresponding recognition results into the artificial neural network, and the network will gradually learn to recognize similar images through self-learning function. The self-learning function is particularly important for forecasting.
It is expected that artificial neural network computers in the future will provide human beings with economic forecasts, market forecasts, and benefit forecasts, and their application prospects are very promising. Second, it has Lenovo storage function. This kind of association can be realized with the feedback network of artificial neural network. Third, it has the ability to find optimal solutions at high speed.
Finding an optimal solution to a complex problem often requires a large amount of calculations. Using a feedback-type artificial neural network designed for a certain problem and utilizing the high-speed computing power of a computer may quickly find an optimal solution.
The outstanding advantages of the artificial neural network: (1) It can fully approach any complex nonlinear relationship; (2) All quantitative or qualitative information is stored in each neuron in the network with equipotential distribution, so it has strong robustness and fault tolerance; (3) Using parallel distributed processing method, it is possible to quickly perform a large number of calculations; (4) Can learn and adapt to unknown or uncertain systems; (5) Can process quantitative and qualitative knowledge at the same time.
Advantages of Human Brain Neural Networks
They are able to outperform almost all other machine learning algorithms. The main advantage of neural networks is that they are able to outperform almost all other machine learning algorithms and are highly robust and fault-tolerant because information is distributed among the neurons within the network.
The human brain neural network is a neural network that simulates the human brain in order to achieve artificial intelligence-like machine learning technology. The neural network in the human brain is a very complex organization. It is estimated that there are as many as 100 billion neurons in the adult brain. .
Artificial Neural Network Evaluation Method
Artificial neuron is the basic processing unit of artificial neural network, and an important part of artificial intelligence is artificial neural network. The artificial neural network is a mathematical model that simulates the biological neuron system, and the information is mainly received through neurons.
First, the artificial neuron uses the connection strength to amplify the generated signal; then, it receives the weighted accumulation of the output of all neurons connected to it; finally, compares the neuron with the weighted sum one by one, and activates when it is greater than the threshold With artificial neurons, signals are sent to neurons in the layer above it is connected, but not vice versa.
An important model of the artificial neural network is the Back-Propagation Model (BP model for short).
For a backpropagation network with n input nodes and m output nodes, the relationship between input and output can be regarded as a mapping from n-dimensional space to m-dimensional space. Due to the large number of nonlinear nodes in the network, it can be highly nonlinear.
(1) The steps of neural network evaluation method The purpose of using neural network to evaluate reclamation potential is to produce an expected evaluation result for the input of a certain index. In the process, the weight of the connection arc of the network needs to be continuously adjusted. . (1) Initialize the weights of all connected arcs.
In order to ensure that the network will not be saturated and abnormal, it is generally set to a small random number. (2) Input a set of training data into the network, and calculate the output value of the network.
(3) Calculate the deviation between the expected value and the output value, and then reversely calculate from the output layer to the first hidden layer, and adjust the weight of each arc to make it develop in the direction of reducing the deviation.
(4) Repeat the above steps and repeatedly calculate each group of training data in the training set until the deviation between the two reaches a level that can be recognized. (2) Establishment of artificial neural network model (1) Determine the number of input layers.
According to the actual situation of the evaluation object, the number of input layers is the number of selected evaluation indicators. (2) Determine the number of hidden layers.
Usually the most ideal neural network has only one hidden layer. The input signal can be separated by hidden nodes and then combined into a new vector. Its operation is fast, which can simplify complex things and reduce unnecessary troubles. (3) Determine the number of hidden layer nodes.
According to the empirical formula: In the disaster-damaged land reclamation formula: j—the number of hidden layers; n—the number of input layers; m—the number of output layers. The structure of the artificial neural network model is shown in Figure 5-2.
Figure 5-2 Structure diagram of artificial neural network (according to Zhou Lihui, 2004) (3) The calculation of artificial neural network inputs the index information (X1, X2, X3, ..., Xn) of the evaluated object, and calculates the actual output value Yj.
The reclamation of disaster-damaged land compares the known output with the calculated output, and modifies the weights and thresholds of K-layer nodes. In the disaster-damaged land reclamation formula: wij——connection weight and threshold of node j in K-1 layer; η——coefficient (0<η<1); Xi——output of node i.
Output result: Cj=yj(1-yj)(dj-yj) (5-21) where: yj—the actual output value of node j; dj—the expected output value of node j.
Because it is impossible to compare the output of hidden nodes, it can be deduced: In the disaster-damaged land reclamation formula: Xj—the actual output value of node j.
It is a process of taking turns to replace, each iteration will adjust the W value, so after repeated replacement, it will not stop until the deviation between the calculated output value and the expected output value is within the allowable value range.
Using the artificial neural network method to evaluate the reclamation potential is actually to establish the mapping relationship between land reclamation impact evaluation factors and reclamation potential.
As long as the selected network structure is appropriate, the above-mentioned mapping relationship can be infinitely approached by using the approximation of the artificial neural network function, so it is appropriate to use the artificial neural network method to evaluate the reclamation potential of disaster-damaged land.
(4) Advantages and disadvantages of the artificial neural network method Compared with other methods, the artificial neural network method has the following advantages: (1) It uses the optimal training principle to perform repeated calculations, and continuously debugs the neural network structure until a relatively stable the result of.
Therefore, adopting this method to evaluate reclamation potential can eliminate many subjective factors and ensure the authenticity and objectivity of the evaluation results of reclamation potential. (2) The error of the obtained evaluation result is relatively small, and the system error can be reduced through repeated iterations, which can meet any accuracy requirements.
(3) Good dynamics, by increasing the number of reference samples and over time, dynamic tracking comparison and deeper learning can be realized.
(4) It is based on nonlinear functions, which is closer to the complex nonlinear dynamic economic system, and can reflect the reclamation potential of disaster-damaged land more truly and accurately, and is more applicable than traditional evaluation methods.
But the artificial neural network also has certain deficiencies: (1) The artificial neural network algorithm adopts an optimization algorithm, which continuously adjusts the weights between the connected neurons through iterative calculations until the global optimization is achieved.
However, the error surface is quite complicated, and the neural network will fall into a local minimum point if you are not careful during the calculation process.
(2) The error propagates backwards through the output layer. The more hidden layers are, the more inaccurate the reverse propagation deviation is when it is close to the input layer, and the evaluation efficiency is also affected to a certain extent, and the convergence speed is not timely. It caused deviation in the evaluation results of reclamation potential in individual areas.
What are the characteristics of artificial neural network?
The outstanding advantages of artificial neural network (1) It can fully approach any complex nonlinear relationship; (2) All quantitative or qualitative information is stored in each neuron in the network in equipotential distribution, so it has strong robustness and Fault tolerance; (3) Using parallel distributed processing methods, making it possible to quickly perform a large number of calculations; (4) Can learn and adapt to unknown or uncertain systems; (5) Can process quantitative and qualitative knowledge at the same time.
Thinking: What are the advantages of neural network over multiple regression?
Has the ability to learn. 1. For example, when realizing image recognition, you only need to input many different image templates and corresponding recognition results into the artificial neural network, and the network will gradually learn to recognize similar images through the self-learning function. The self-learning function is particularly important for forecasting.
It is expected that artificial neural network computers in the future will provide human beings with economic forecasts, market forecasts, and benefit forecasts, and their application prospects are very promising. 2. With Lenovo storage function. This kind of association can be realized with the feedback network of artificial neural network. 3. Ability to find optimal solutions at high speed.
Finding an optimal solution to a complex problem often requires a large amount of calculations. Using a feedback-type artificial neural network designed for a certain problem and utilizing the high-speed computing power of a computer may quickly find an optimal solution.
Is the advantage of artificial neural network translation technology to understand language and generate translation?
(1) With self-learning function. For example, when realizing image recognition, we only need to input many different image templates and corresponding recognition results into the artificial neural network, and the network will gradually learn to recognize similar images through self-learning function. The self-learning function is particularly important for forecasting.
It is expected that artificial neural network computers in the future will provide human beings with economic forecasts, market forecasts, and benefit forecasts, and their application prospects are very promising. (2) With association storage function. This kind of association can be realized with the feedback network of artificial neural network. (3) It has the ability to find the optimal solution at high speed.
Finding an optimal solution to a complex problem often requires a large amount of calculations. Using a feedback-type artificial neural network designed for a certain problem and utilizing the high-speed computing power of a computer may quickly find an optimal solution.
Extended information: The artificial neural network with its unique nonlinear adaptive information processing ability overcomes the intuition of the shortcomings of traditional artificial intelligence methods, such as pattern recognition, speech recognition, and unstructured information processing, and makes it successfully applied to neuroexpert systems , pattern recognition, intelligent control, combination optimization and forecasting.
The combination of artificial neural network and other traditional methods will promote the development of artificial intelligence and information processing technology.
In recent years, artificial neural networks have further developed on the road of simulating human cognition, and combined with fuzzy systems, genetic algorithms and evolutionary mechanisms to form computational intelligence, which has become an important direction of artificial intelligence and will be developed in practical applications.
The application of information geometry in artificial neural network research has opened up a new way for the theoretical research of artificial neural network. Research on neural computers is advancing rapidly, and some products have entered the market. The photoelectric neural computer provides good conditions for the development of artificial neural networks.