CN107609636A - A kind of polygamma function correspondingly exports the design method of feedback function artificial neuron - Google Patents

Abstract

A kind of polygamma function correspondingly exports the technical field of the design method of feedback function artificial neuron,It is to belong to artificial intelligence,Bionics,The technical field of circuit design,Major technique is that artificial neuron is inputted by multichannel,When accumulated value is less than minimum threshold values,Artificial neuron,It will not be activated,When cumulative value exceedes the threshold values of setting,Artificial neuron is activated,Threshold values is passed to activation primitive collection,Activation primitive collection sets multiple activation primitives,These activation primitives are activated entirely,Each activation primitive exports oneself operation result,Activation primitive collection connects with end-apparatus is selected,How many activation primitive connects with regard to how many bar,Pass through the setting to selecting end-apparatus,It is feedback that some activation primitives, which can be set,,Some activation primitives are directly passed to next layer of artificial neuron,Some activation primitives therein can also be set,Feedback and directly transmission exist simultaneously.

Description

A kind of polygamma function correspondingly exports the design method of feedback function artificial neuron

Technical field

A kind of polygamma function correspondingly exports the technical field of the design method of feedback function artificial neuron, is to belong to artificial intelligence Can, bionics, the technical field of circuit design, major technique is that artificial neuron is inputted by multichannel, when accumulated value is less than most During small threshold values, artificial neuron, it will not be activated, when cumulative value exceedes the threshold values of setting, artificial neuron is activated, valve Value passes to activation primitive collection, and activation primitive collection sets multiple activation primitives, and these activation primitives are activated entirely, each activation Function exports oneself operation result, and activation primitive collection connects with end-apparatus is selected, and how many activation primitive connects with regard to how many bar, leads to The setting to selecting end-apparatus is crossed, it is feedback that can set some activation primitives, and some activation primitives are to be directly passed to next layer Artificial neuron's, some activation primitives therein can also be set, and feedback and directly transmission exist simultaneously.

Background technology

Neuron is the elementary cell for forming brain, and the brain of the mankind is that have thousands of individual neurons according to certain rule Form, for the mankind in order to simulate human brain, the design to artificial neuron is the most important thing, has artificial neuron to form people Work network, artificial neural network are a kind of mathematical modulos for the structure progress information processing that application is similar to cerebral nerve cynapse connection Type.In this model, composition network is coupled to each other between substantial amounts of artificial neuron, i.e. " neutral net ", to reach processing The purpose of information.A kind of kinetic simulation for the distributed parallel information processing algorithm structure for imitating animal nerve network behavior feature Type., with multichannel input stimulus are received, the part that " excitement " output is produced when exceeding certain threshold value by weighted sum is dynamic to imitate for it The working method of thing neuron, and the weight coefficient of the structure being coupled to each other by these neural components and reflection strength of association makes Its " collective behavior " has the various complicated information processing functions.Particularly it is this macroscopically have robust, it is fault-tolerant, anti-interference, The formation of the flexible and strong function such as adaptability, self study can not only be updated by component performance, and pass through Complicated interconnecting relation is achieved, thus artificial neural network is a kind of connection mechanism model, has many of complication system Key character.Artificial neural network be applied to signal transacting, data compression, pattern-recognition, robot vision, knowledge processing and its Using prediction, evaluation and the combinatorial optimization problem such as decision problem, scheduling, route planning.It can in Control System Design For simulating controlled device characteristic, search and study control law, realizing fuzzy and intelligent control, therefore to the design of neuron Very important, because fairly obvious, the shape of neuron is very more, although the mankind classify it, neuron has Thousands of kinds, therefore different neurons also possesses different functions, the present invention simply devises a kind of artificial neuron, existing The design very simple of some neurons is single, is exactly all inputs and multiplied by weight, is then added up, subtract valve Value, then sets activation primitive, passes to next layer of neuron.

The content of the invention

The brain of people is that many neurons are formed, therefore neuron is the elementary cell of neutral net, fairly obvious, nerve First enormous amount, just there are the neuron of different shape, structure, physiologic character and function, neuron in the different parts of human body Shape it is very strange very more, although the mankind classify to it, neuron has millions upon millions of kinds, therefore different nerves Member also possesses different functions, and the present invention simply devises a kind of artificial neuron, due to existing neuron design very It is simple single, exactly all inputs and multiplied by weight are added up, threshold values is subtracted, then activation primitive is set, pass to Next layer of neuron, a network is so formed, and so simple design solves many forefathers of the mankind and can not solved The problem of, tremendous influence is produced to All Around The World, but a kind of this simply artificial neuron meta structure most simply, in real world The various shapes of neuron, various functions, therefore will invention various functions artificial neuron design, it is a kind of Polygamma function correspondingly exports the design method of feedback function artificial neuron, it is characterized in that：Polygamma function correspondingly exports feedback function people Work neuron is by input, artificial neuron, selects end-apparatus, output end, and feedback line forms, and input is as defeated in neuron Enter end, receive the input of upper level artificial neuron or the input by other equipment, the effect of artificial neuron is input Added up after value and multiplied by weight, if cumulative value is less than threshold values, then artificial neuron would not be activated, and not appoint What reacts, if cumulative value is more than threshold values, then artificial neuron is activated, and cumulative value is just passed to activation primitive Collect, all activation primitives are activated in activation primitive collection, and each activation primitive exports oneself operation result, activation primitive collection Connected with end-apparatus is selected, how many activation primitive connects with regard to how many bar, by the setting to selecting end-apparatus, can set some sharp Function living is feedback, and some activation primitives are directly passed to next layer of artificial neuron, can also set it is therein certain A little activation primitives, directly feedback and transmission exist simultaneously, and wherein artificial neuron uses following design, and it is made up of 2 parts, and 1 It is accumulator, its effect is exactly that input is added up, if reaches threshold values, if it exceeds threshold values, just cumulative this Value passes to activation primitive collection, and all activation primitives are activated simultaneously, and 2 activation primitive collection are made up of multiple activation primitives, and one More than threshold values, these activation primitives are activated the cumulative value of denier entirely, are passed to by respective circuit and select end-apparatus, select end-apparatus use Following design, it is connected with activation primitive collection, and how many activation primitive is being selected in end-apparatus in advance with regard to how many bar connection line That circuit is designed to connect with those ports, for example, activation primitive f (x1) it can be exported from o-1.o-2, be fed back to i- 1.1-2, f (x2) are exported by i-4.i-5, and f (x3) is exported by i-6.i-7.i-8.i-9, and f (x4) is exported by i-11.i-9.i-10, I-11 is directly transferred to next layer of neuron by feedback i-9.i-10.

Brief description of the drawings

Fig. 1 is the structure principle chart that polygamma function correspondingly exports feedback function artificial neuron, i-1.1-2.i-3.i-4.i- 5.i-6.i-7.i-8.i-9.i-10.i-11.i-12 represents input, and this input is a lot, and it is for generation to draw 12 here Table acts on, and o-1.o-2.o-3.o-4.o-5.i-6.i-7.i-8.i-9.i-10.i-11.i-12 represents output end, this output End is a lot, and it is for role of delegate to draw 12 here, and a-1 represents artificial neuron, and a-2 represents the insideAccumulator, a-3 generations Table activation primitive collection, f (x1) .f (x2) .f (x3) .f (x4) represent the activation primitive of the inside, and b-1 is represented and selected end-apparatus, b-2. b- 3.b-4.b-5 represents activation primitive collection and selects the line of end-apparatus, and how many activation primitive is with regard to how many line, each activation letter The corresponding line of number, b-6.b-7.b-8.b-9 represent feedback line.

Implementation

The brain of people has thousands of neuron, and some neurons are fed back by exporting, to adjust neuron oneself, and Next layer of neuron can be directly passed to, because this is a kind of common scenario, therefore the present invention is also according to such case, wound A kind of artificial neuron is made, polygamma function, which correspondingly exports feedback function artificial neuron, to be by input, artificial neuron, select end Device, output end, feedback line composition, input such as the input of neuron, receive upper level artificial neuron input or By the input of other equipment, the effect of artificial neuron is added up after value and multiplied by weight input, if cumulative Value is less than threshold values, then artificial neuron would not be activated, without any reaction, if cumulative value is more than threshold values, then Artificial neuron is activated, and cumulative value is just passed to activation primitive collection, and all activation primitives are swashed in activation primitive collection Living, each activation primitive exports oneself operation result, and activation primitive collection connects with end-apparatus is selected, and how many activation primitive just has How many connections, by the setting to selecting end-apparatus, it is feedback that can set some activation primitives, and some activation primitives are direct Next layer of artificial neuron is passed to, some activation primitives therein can also be set, feedback and directly transmission exist simultaneously, Such artificial neuron and the artificial neuron of other functions are networked, form an artificial brain, it is possible to reach imitation The function of human brain, it is the form that feedback function is correspondingly exported using polygamma function, therefore due to the artificial neuron of the present invention More functions can be realized, fewer artificial neuron of the invention can be used, reach sufficiently complex network function.

Claims (1)

1. a kind of polygamma function correspondingly exports the design method of feedback function artificial neuron, it is characterized in that：Polygamma function is corresponding to be exported Feedback function artificial neuron is by input, artificial neuron, selects end-apparatus, output end, feedback line composition, input as The input of neuron, receive the input of upper level artificial neuron or the input by other equipment, the effect of artificial neuron It is to be added up after value and multiplied by weight input, if cumulative value is less than threshold values, then artificial neuron would not be by Activation, without any reaction, if cumulative value is more than threshold values, then artificial neuron is activated, just cumulative value transmission Give activation primitive collection, all activation primitives are activated in activation primitive collection, and each activation primitive exports oneself operation result, Activation primitive collection connects with end-apparatus is selected, and how many activation primitive connects with regard to how many bar, can be with by the setting to selecting end-apparatus It is feedback to set some activation primitives, and some activation primitives are directly passed to next layer of artificial neuron, can also set Some activation primitives therein are put, feedback and directly transmission exist simultaneously, and wherein artificial neuron is using following design, and it is by 2 Part forms, and 1 is accumulator, and its effect is exactly that input is added up, if reach threshold values, if it exceeds threshold values, just This cumulative value passes to activation primitive collection, and all activation primitives are activated simultaneously, and 2 activation primitive collection have multiple activation letters Array is into once cumulative value is more than threshold values, these activation primitives are activated entirely, are passed to by respective circuit and select end-apparatus, selected End-apparatus is using following design, and it is connected with activation primitive collection, and how many activation primitive is selecting end with regard to how many bar connection line That circuit is designed in device in advance to connect with those ports, for example, activation primitive f (x1) it can be exported from o-1.o-2, it is anti- I-1.1-2 is fed to, f (x2) is exported by i-4.i-5, and f (x3) is exported by i-6.i-7.i-8.i-9, and f (x4) is by i-11.i-9.i- 10 outputs, i-11 are directly transferred to next layer of neuron by feedback i-9.i-10.