Artificial intelligence decision making neuro network core system and information processing method using the same

Artificial intelligence decision making neuro network core system and information processing method using the same include an electronic device linking to a unsupervised neural network interface module, a asymmetric hidden layers input module linking to the unsupervised neural network interface module and a neuron module formed with tree-structured data, a layered weight parameter module linking to the neuron module formed with tree-structured data and an non-linear PCA (Principal Component Analysis) module, an input module of the lead backpropagation unit linking to the non-linear PCA module and a tuning module, an output module of the lead backpropagation unit linking to tuning module and the non-linear PCA module; when the electronic device receives raw data, processing and learning the raw data via all the modules, and updating programs to generate decision results that accommodate a variety of scenarios, in order to elevate the reference value and practicality of the decision result.

CROSS-REFERENCE TO RELATED APPLICATION

This non-provisional application claims priority under 35 U.S.C. § 119(a) on Patent Application No(s). 108131431 filed in Taiwan, R.O.C. on Aug. 30, 2019, the entire contents of which are hereby incorporated by reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present application relates to an artificial intelligence decision making system and a method using the same, particularly to an artificial intelligence decision making neuro network core system and an information processing method using the same.

2. Description of the Related Art

The present artificial intelligence decision making systems often adopt a single neuro network of some sort, and the neuro network of interest is often implemented by neurons with a traditional linear data structure in the neuro network.

However, the above-mentioned traditional neuro network performs its data training with linear data structure, and its result is one note, and the decision-making style that comes with is also highly similar to other neuro network of the same kind, which makes the generated decision result non-distinguishable for those applied scenarios with a bit of differences among them. The non-distinguishable decision result also has low value, low reference and low usability, which further effects the benefits of decision making.

BRIEF SUMMARY OF THE INVENTION

In regard with the above-mentioned deficiency of present arts, a main purpose of the present application is to provide an artificial intelligence decision making neuro network core system and an information processing method using the same, via the neuro network, combining an non-linear analysis and feedback mechanism to provide decision results that accommodate a varieties of scenarios, in order to elevate the value and practicality of the decision result.

One main technical means to achieve the above-mentioned objective is to utilize an electronic device installed with one or more application programs that implements the artificial intelligence decision making neuro network core system and performs the information processing method. The electronic device performs the following steps:

receiving a raw data;

generating a pre-processed raw data according to the raw data;

generating a tree-structured data according to the pre-processed raw data;

performing weight computing on tree-structured data, to obtain a weight parameter data;

performing a non-linear computing program according to the weight parameter data, to generate a non-linear computing data;

performing tuning on the non-linear computing data, to generate a data tuning feedback information;

updating the non-linear computing program, and outputting a corresponding decision result information according to the data tuning feedback information.

According to the above-mentioned method, after layers upon layers data processing on the raw data, performing data tuning on the non-linear computing data, to generate the data tuning feedback information, and feedback updating the non-linear program according to the data tuning feedback information, and outputting corresponding decision result information, can make the present application proposed method applying to different raw data, and make outputted decision result information that accommodate a varieties of scenarios, in order to elevate the value and practicality of the decision result.

Another main technical means to achieve the above-mentioned objection is to provide the aforementioned artificial intelligence decision making neuro network core system, comprising:

an electronic device, receiving a raw data;

an unsupervised neural network interface module, linking to the electronic device;

an asymmetric hidden layers input module, receiving the raw data via the unsupervised neural network interface module, and performing a data pre-processing program to generate a pre-processed raw data;

a neuron module formed with tree-structured data, linking to the asymmetric hidden layers input module, comprising multiple neuron nodes, and performing a data processing program according to the received pre-processed raw data, to generate a tree-structured data;

a non-linear PCA (Principal Component Analysis) module, linking to the layered weight parameter module, and performing a non-linear computing program according to the weight parameter data to generate a non-linear computing data;

an input module of the lead backpropagation unit, linking to the non-linear PCA module;

a tuning module, linking to the input module of the lead backpropagation unit and the non-linear PCA module, and obtaining the non-linear computing data via the input module of the lead backpropagation unit, and performing a data tuning program according to the non-linear computing data to generate and output a data tuning feedback information;

an output module of the lead backpropagation unit, linking to the tuning module and the non-linear PCA module;

wherein, the output module of the lead backpropagation unit obtains the data tuning feedback information via the tuning module, and sends the data tuning feedback information back to the non-linear PCA module, to update the non-linear computing program, and to output a decision result information.

As can be known from the aforementioned system, after the raw data received by the electronic device transmitted the asymmetric hidden layers input module via the neuro network interface, via the neuron module formed with tree-structured data, the layered weight parameter module, the non-linear PCA module performs layers of data processing on the raw data, to output the non-linear computing data to the tuning module. After the tuning module performs data processing according to the non-linear computing data, outputs the data tuning feedback information to the non-linear PCA module via the output module of the lead backpropagation unit to update the non-linear computing program, and to output corresponding decision result. By processing on the data, and sending the tuning result information back to the non-linear PCA module and updating the non-linear computing program, making a decision result provided by the present application system can accommodate a variety of scenarios, in order to elevate the value and practicality of the decision result.

DETAILED DESCRIPTION OF THE INVENTION

Regarding a preferable embodiment of the present application of the artificial intelligence decision making neuro network core system, please refer toFIGS.1and2, comprising an electronic device10. The electronic device10is installed with an operating system and performs one or more application programs on the electronic device10, the electronic device10includes an unsupervised neural network interface module11, an asymmetric hidden layers input module12, a neuron module formed with tree-structured data13, a layered weight parameter module14, an non-linear PCA (Principal Component Analysis) module15, an input module of the lead backpropagation unit16, a tuning module17, and an output module of the lead backpropagation unit16′. The unsupervised neural network interface module11links to the electronic device10and the asymmetric hidden layers input module12, the neuron module formed with tree-structured data13links to the asymmetric hidden layers input module12and the layered weight parameter module14, the non-linear PCA module15links to the layered weight parameter module14and the input module of the lead backpropagation unit16, the tuning module17links to the input module of the lead backpropagation unit16and the output module of the lead backpropagation unit16′, and the output module of the lead backpropagation unit16′ links to the non-linear PCA module15.

In the embodiment, the electronic device10may be a traditional computer, such as a desktop computer or a laptop computer, a tablet, a server or a quantum computer or any electronic devices that are capable of performing programs, calculations, and data processing.

Regarding another embodiment of the present application of the artificial intelligence decision making neuro network core system, please refer toFIG.2. The system further includes a tree search module18. The tree search module18links to the neuron module formed with tree-structured data13, the tuning module17and the output module of the lead backpropagation unit16′. The tree search module18has a duplicate-node-avoid unit181and a UCT unit182.

In the embodiment, the tree-shaped data structure for neuron module13further includes a cross-neuron computing unit131and a neuron weight updater132. The layered weight parameter module14further includes an activation function control module141. The activation function control module141has an activation function switch1411and a Laplace transformer1412. The tuning module17has a residual analyzer171, a self-tuning function unit172, a training breaker173and a state register174. Regarding the specifications of the aforementioned cross-neuron computing unit131, neuron weight updater132, activation function control module141, residual analyzer171, self-tuning function unit172, training breaker173, state register174, and the tree search module18will be detailed later.

Please refer toFIGS.1and3, when a user inputs a raw data via the electronic device10, the raw data is received by the unsupervised neural network interface module11, so as to input the raw data into a neuro network to perform analysis, processing, learning, and decision making thereupon. The unsupervised neural network interface module11sends the raw data to the asymmetric hidden layers input module12. The asymmetric hidden layers input module12performs a data pre-processing program on the raw data, to generate a pre-processed raw data, and to output the pre-processed raw data to the neuron module formed with tree-structured data13. Wherein, the asymmetric hidden layers input module12has multiple neurons121, and the asymmetric hidden layers input module12performs the data pre-processing program on the raw data, to configure and arrange the raw data, and to plan the routing between the neurons121according to the number of the neurons121, to generate the pre-processed raw data.

Furthermore, please refer toFIGS.1and4, the neuron module formed with tree-structured data13performs a data processing program on the pre-processed raw data, to generate a tree-structured data, wherein the neuron module formed with tree-structured data13performs the data processing program according to the pre-processed raw data, the neurons121, to generate the tree-structured data, and to transmit the tree-structured data to the layered weight parameter module14; wherein the tree-structured data includes a plurality of neuron tree-shaped data structures121′ constituted by the neurons121.

Please refer toFIGS.1and4for further details, the layered weight parameter module14performs a weight parameter computing program on the tree-structured data, to obtain a weight parameter data, and to output the weight parameter data to the non-linear PCA module15; wherein the layered weight parameter module14respectively sets a corresponding weight WL1, WL2. . . to each node of the neuron tree-shaped data structure121′ in the tree-structured data, to build up weight parameter of each node. Furthermore, the activation function control module141of the layered weight parameter module14has multiple activation functions, the activation function control module141performs a Laplace transform computing according to the activation functions and the weight parameter data via the Laplace transformer1412, to generate a Laplace transform computing result, and the activation function control module141switches to corresponding activation function according to the Laplace transform computing result via the activation function switch1411, to generate an activation configuration data corresponding to the weight parameter data, and thereby further elevating the adoptability of the weight parameter data, and collecting data more efficiently, in order to obtain more corresponding data, and providing them to the non-linear PCA module15to perform the non-linear computing program, to generate more accurate and more reference worthy non-linear computing data.

Please refer toFIGS.1and4, the non-linear PCA module15further performs a non-linear computing program on the weight parameter data according to received weight parameter data, to generate a non-linear computing data, and to output the non-linear computing data to the input module of the lead backpropagation unit16, so as to input data into the tuning module17via the input module of the lead backpropagation unit16; wherein non-linear computing program of the non-linear PCA module15performs a non-linear regression analysis on the weights in the weight parameter data after receiving the weight parameter data, to obtain variables' correlation that can be used in the analysis of weight values, and to generate a regression analysis function model that can be used in the analysis of the weight values, and thereby analyzing, calculating the weight parameter data, performing the non-linear computing program on the weight parameter data via the non-linear PCA module15, and generated non-linear calculation will make data hard to be embezzled, and can make the data adoptable to different scenarios.

Please refer toFIGS.1and5, the tuning module17performs a data tuning program on the non-linear computing data according to the received non-linear computing data, to generate a data tuning feedback information, and to send the data tuning feedback information to the output module of the lead backpropagation unit16′, so as to send updating the non-linear computing program to the non-linear PCA module15via the output module of the lead backpropagation unit16′; wherein when the residual analyzer171of the tuning module17receives the non-linear computing data, it performs a residual value computing program according to one or more residual value (difference) in the non-linear computing data and the regression analysis function model, to generate a residual analysis data. The residual analysis data includes a residual Gaussian distribution, a residual variable, etc. The residual analyzer171send the residual analysis data to the self-tuning function unit172. The self-tuning function unit172performs a residual tuning function computing program according to the non-linear computing data and the residual analysis data, to generate the data tuning feedback information. In addition, the training breaker173receives the data tuning feedback information, and determines whether a training break condition is met according to the data tuning feedback information. If it determines the training break condition is met, the training breaker173generates a training break information, and outputs the training break information to the non-linear PCA module15via the output module of the lead backpropagation unit16′, to break a training session. Furthermore, the state register174stores multiple break configuration transit data. The training breaker173further generates corresponding training break information according to the break configuration transit data in the state register174.

Furthermore, please refer toFIGS.1and5, after the output module of the lead backpropagation unit16′ receives the data tuning feedback information, the output module of the lead backpropagation unit16′ performing data processing according to received data tuning feedback information, raw data, tree-structured data, weight parameter data, non-linear computing data, to generate a corresponding decision result information, for the user's reference.

Please refer toFIGS.1and5, when the output module of the lead backpropagation unit16′ sends the data tuning feedback information to the non-linear PCA module15, to update the non-linear computing program, the non-linear PCA module15sends the data tuning feedback information to the layered weight parameter module14, the layered weight parameter module14sends the data tuning feedback information to the neuron module formed with tree-structured data13. The cross-neuron computing unit131of the tree-shaped data structure for neuron module13performs a neuron data updating program according to received data tuning feedback information, to generate a corresponding neuron update data, and updates the tree-structured data via the neuron weight updater132and sends the weight parameter data to the asymmetric hidden layers input module12to update the pre-processed raw data. That is to say, updating the weight of the neurons of the neuron tree-shaped data structure121′ in the tree-structured data, and thereby keeps tuning each related parameter of the neurons121according to the data tuning feedback information during analysis and training, to push the limit of traditional decision making result by updating neuron, and to enhance training efficiency and accuracy.

Please refer toFIGS.2and5, the tree search module18determines whether duplicated neuron nodes exist according to the neuron tree-shaped data structure121′ of the tree-structured data via the duplicate-node-avoid unit181. If the tree search module18determines duplicate neuron nodes exist, it generates a duplicate node information, to generate a corresponding training break information, and outputs the corresponding training break information to the non-linear PCA module15via the output module of the lead backpropagation unit16′, to break a training session. Furthermore, the UCT (Upper Confidence bounds to Trees) unit182performs data processing according to corresponding data tuning feedback information, raw data, tree-structured data, weight parameter data, non-linear computing data, to generate a corresponding UCT information, and the UCT unit182determines whether the data tuning feedback information conforms to (exceeds) the UCT information. If it's not the conformed case, the UCT unit182generates a corresponding training break information, and outputs the corresponding training break information to the non-linear PCA module15via the output module of the lead backpropagation unit16′, to break a training session, wherein the UCT unit182uses a UCT (Upper Confidence bounds to Trees) algorithm, and cooperates with multiple built-in determining mechanism of the UCT unit182, to balance the utilization of deeper layer's data variations and the exploring with lesser data shift, for data processing in an efficiency way.

As can be known from the above, by processing data, and updating all sorts of parameters of the present application according to the tuning result information, the system of the present application provides a decision result that can accommodate all sorts of scenarios, and thereby increasing the reference value and practicality of the decision result.

According to the above, the present application further includes an aforementioned artificial intelligence decision making neuro network core method. Please refer toFIGS.1and6, which illustrates an electronic device10with multiple application programs installed thereon, and the electronic device10performs the following steps:

receiving a raw data (s31);

generating a pre-processed raw data according to the raw data (s32);

generating a tree-structured data according to the pre-processed raw data (s33);

performing weight computing on the tree-structured data, to obtain a weight parameter data (s34);

performing a non-linear computing program according to the weight parameter data, to generate a non-linear computing data (s35);

performing a data tuning program on the non-linear computing data, to generate a data tuning feedback information (s36);

updating the non-linear computing program, and outputting a corresponding decision result information according to the data tuning feedback information (s37).

When the step of performing weight computing on the tree-structured data to obtain a weight parameter data is performed (s34), the method further comprises steps:

performing a Laplace transform computing on the weight parameter data, to generate a Laplace transform computing result;

generating an activation configuration data corresponding to the weight parameter data according to multiple activation functions and the Laplace transform computing result.

When the step of performing a data tuning program on the non-linear computing data to generate a data tuning feedback information is performed (s36), the method further comprises steps:

performing a residual value computing program, to generate a residual analysis data according to the non-linear computing data;

performing a residual tuning function computing program according to the residual analysis data and the non-linear computing data, to generate the data tuning feedback information.

Furthermore, after the step of performing a residual tuning function computing program according to the residual analysis data and the non-linear computing data, to generate the data tuning feedback information is performed, the method further comprises steps:

determining whether a training break condition is met according to the data tuning feedback information;

if so, generating a training break information; or

determining whether duplicate neuron nodes exist according to the tree-structured data;

if so, generating a duplicate node information, and generate a corresponding training break information as well.

Furthermore, when the step of updating the non-linear computing program, and outputting a corresponding decision result information according to the data tuning feedback information (s37) is performed, the method further comprises steps:

performing a neuron data updating program according to the data tuning feedback information, to generate a corresponding updated neuron data;

updating the tree-structured data and the weight parameter data according to the updated neuron data.

Furthermore, when the step of determining whether a training break condition is met according to the data tuning feedback information is performed, the method further comprises steps:

performing data processing according to corresponding data tuning feedback information, raw data, tree-structured data, weight parameter data, non-linear computing data, to generate a corresponding UCT information;

determining whether the data tuning feedback information conforms the UCT information;

if it's not the conformed case, generating a corresponding training break information.