CN105427138A - Neural network model-based product market share analysis method and system - Google Patents
Neural network model-based product market share analysis method and system Download PDFInfo
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Abstract
The invention belongs to the Internet communication technical field and relates to a neural network model-based product market share analysis method and system which can perform predictive assessment on the market share of complex products so as to provide a predictive planning base for the production and marketing of the products. The neural network model-based product market share analysis system is characterized in that the neural network model-based product market share analysis system is provided with an information processing server and more than two user terminals, wherein the information processing server is connected with the more than two user terminals; an existing user prediction unit and a potential user prediction unit are arranged in the information processing server; and the user terminal is provided with an information receiving and historical data storage unit, a data screening unit and a data uploading/downloading unit, wherein the information receiving and historical data storage unit stores the preferences of terminal users. Compared with the prior art, the method and system can predict the market share of the complex products according to the number of existing users and the preferences of the users, and have the advantages of high response speed, accurate estimation and the like.
Description
Technical field:
The present invention relates to Internet communication technology field, specifically one can carry out expection assessment to complex product market share, and then sells for production and provide the expection product market share analytical approach based on neural network of foundation of planning and system.
Background technology:
The grading planning of complex product needs to complete according to market forecast information, and the accuracy of market forecast information is one of necessary condition that in project period, investment is estimated.Present stage, for the analysis of market share mainly through realizing the analysis mining of market information in the past, the selection of Forecasting Methodology is directly connected to the degree of accuracy predicted the outcome.Traditional Forecasting Methodology is a lot, comprise trend extrapolation, growth-curve approaches etc., but predicting the outcome of these Forecasting Methodologies there will be following problem: predict the outcome and be level and smooth curve, cannot go out the fluctuation that business causes with season and other external environment conditions by directviewing description.
Artificial neural network (ArtificialNeuralNetworks, ANN) system occurs after the forties in 20th century.It is formed by connecting by the connection weights that numerous neurons is adjustable, has the features such as massively parallel processing, distributed information storage, good self-organization self-learning capability.BP (BackPropagation) algorithm is also called error backpropagation algorithm, is the learning algorithm of a kind of supervised in artificial neural network.BP neural network algorithm can approach arbitrary function in theory, and basic structure is made up of nonlinearities change unit, has very strong non-linear mapping capability.And the parameter such as the learning coefficient of the middle number of plies of network, the processing unit number of each layer and network can set as the case may be, dirigibility is very large, all has a wide range of applications in many fields such as optimization, signal transacting and pattern-recognition, Based Intelligent Control, fault diagnosises.
Summary of the invention:
The present invention is directed to the shortcoming and defect existed in prior art, propose one and can carry out expection assessment to complex product market share, and then sell for production the expection product market share analytical approach based on neural network of foundation of planning and system are provided.
The present invention can be reached by following measures:
Based on a product market share analytical approach for neural network, it is characterized in that comprising the following steps:
Step 1: obtain the number of users in a period of time of somewhere, and for influential 5 indexs of number of users in business, comprising: market share, product popularity rate, monthly festivals or holidays number of days, disposal income of Chinese people and resident population; Number of users and 5 indexs are normalized;
Step 2: set up neural network model; Described neural network model comprises four layers, and ground floor has 1 neuron, and the second layer has 1 neuron, and third layer has 6 neurons, and the 4th layer has 1 neuron; The neuron of ground floor is month sequence, and the neuron of the second layer is network parameter initial value, and third layer 6 neurons are the number of users in corresponding month in ground floor month sequence and corresponding 5 indexs, and the 4th layer be the number of users of the prediction of output; Wherein in the first level, to month modeling time series, month sequence data is converted into the differential equation;
Step 3: training and testing is carried out to neural network model;
Step 4: utilize the number of users being predicted districts and cities by the neural network model of test, wherein profit first inputs between detection data normalization to 0 and+1 again, and obtains the predicted value of number of users after the output valve after network operations is carried out renormalization;
Step 5: obtain user preference information, user preference information is uploaded to information server with character string forms;
Step 6: user preference information is excavated, comprise: calculate the text relevant eigenwert of the first character string and the second character string and the semantic dependency eigenwert of the first character string and the second character string, then with logic-based regression model, described text relevant eigenwert and semantic dependency eigenwert are fitted to the correlative character value of the first character string and the second character string;
Step 7: the Output rusults according to step 6 obtains potential user's quantity, exports market forecast number of users value after merging with step 4 Output rusults.
The invention allows for a kind of product market share analytic system based on neural network, it is characterized in that being provided with netscape messaging server Netscape and plural user terminal, wherein netscape messaging server Netscape is connected with plural user terminal respectively, be provided with existing user in described netscape messaging server Netscape and estimate unit and potential user estimates unit, described user terminal is provided with receives information history data store unit, data screening unit, data upload/download unit, wherein storage terminal user ' s preference in receives information history data store unit.
Existing user in netscape messaging server Netscape of the present invention estimates unit and comprises data acquisition module, data preprocessing module, data-mining module and data outputting module, described data acquisition module is for obtaining the number of users in a period of time of somewhere, and for influential 5 indexs of number of users in business, comprising: market share, product popularity rate, monthly festivals or holidays number of days, disposal income of Chinese people and resident population; Described data preprocessing module is used for being normalized the data of data acquisition module collection.
Potential user of the present invention estimates unit and comprises character string receiving element, correlative character value computing unit and correlative character value fitting unit, wherein: character string receiving element, for receiving the first character string and the second character string; Correlative character value computing unit, for the semantic dependency eigenwert of the text relevant eigenwert and the first character string and the second character string that calculate the first character string and the second character string; Correlative character value fitting unit, fits to the correlative character value of the first character string and the second character string by described text relevant eigenwert and semantic dependency eigenwert for logic-based regression model.
The present invention compared with prior art, can estimate the market share situation of complex product, have fast response time, estimate the significant advantages such as accurate according to existing number of users and user preference.
Accompanying drawing illustrates:
Accompanying drawing 1 is system chart of the present invention.
Reference numeral: netscape messaging server Netscape 1, user terminal 2, existing user estimate unit 3, potential user estimates unit 4.
Embodiment:
Below in conjunction with accompanying drawing, the present invention is further illustrated.
As shown in drawings, the present invention proposes a kind of product market share analytical approach based on neural network, it is characterized in that comprising the following steps:
Step 1: obtain the number of users in a period of time of somewhere, and for influential 5 indexs of number of users in business, comprising: market share, product popularity rate, monthly festivals or holidays number of days, disposal income of Chinese people and resident population; Number of users and 5 indexs are normalized;
Step 2: set up neural network model; Described neural network model comprises four layers, and ground floor has 1 neuron, and the second layer has 1 neuron, and third layer has 6 neurons, and the 4th layer has 1 neuron; The neuron of ground floor is month sequence, and the neuron of the second layer is network parameter initial value, and third layer 6 neurons are the number of users in corresponding month in ground floor month sequence and corresponding 5 indexs, and the 4th layer be the number of users of the prediction of output; Wherein in the first level, to month modeling time series, month sequence data is converted into the differential equation;
Step 3: training and testing is carried out to neural network model;
Step 4: utilize the number of users being predicted districts and cities by the neural network model of test, wherein profit first inputs between detection data normalization to 0 and+1 again, and obtains the predicted value of number of users after the output valve after network operations is carried out renormalization;
Step 5: obtain user preference information, user preference information is uploaded to information server with character string forms;
Step 6: user preference information is excavated, comprise: calculate the text relevant eigenwert of the first character string and the second character string and the semantic dependency eigenwert of the first character string and the second character string, then with logic-based regression model, described text relevant eigenwert and semantic dependency eigenwert are fitted to the correlative character value of the first character string and the second character string;
Step 7: the Output rusults according to step 6 obtains potential user's quantity, exports market forecast number of users value after merging with step 4 Output rusults.
The invention allows for a kind of product market share analytic system based on neural network, it is characterized in that being provided with netscape messaging server Netscape 1 and plural user terminal 2, wherein netscape messaging server Netscape 1 is connected with plural user terminal 2 respectively, be provided with existing user in described netscape messaging server Netscape 1 and estimate unit 3 and potential user estimates unit 4, described user terminal 2 is provided with receives information history data store unit, data screening unit, data upload/download unit, wherein storage terminal user ' s preference in receives information history data store unit.
Existing user in netscape messaging server Netscape of the present invention estimates unit and comprises data acquisition module, data preprocessing module, data-mining module and data outputting module, described data acquisition module is for obtaining the number of users in a period of time of somewhere, and for influential 5 indexs of number of users in business, comprising: market share, product popularity rate, monthly festivals or holidays number of days, disposal income of Chinese people and resident population; Described data preprocessing module is used for being normalized the data of data acquisition module collection.
Potential user of the present invention estimates unit and comprises character string receiving element, correlative character value computing unit and correlative character value fitting unit, wherein: character string receiving element, for receiving the first character string and the second character string; Correlative character value computing unit, for the semantic dependency eigenwert of the text relevant eigenwert and the first character string and the second character string that calculate the first character string and the second character string; Correlative character value fitting unit, fits to the correlative character value of the first character string and the second character string by described text relevant eigenwert and semantic dependency eigenwert for logic-based regression model;
The present invention compared with prior art, can estimate the market share situation of complex product, have fast response time, estimate the significant advantages such as accurate according to existing number of users and user preference.
Claims (4)
1., based on a product market share analytical approach for neural network, it is characterized in that comprising the following steps:
Step 1: obtain the number of users in a period of time of somewhere, and for influential 5 indexs of number of users in business, comprising: market share, product popularity rate, monthly festivals or holidays number of days, disposal income of Chinese people and resident population; Number of users and 5 indexs are normalized;
Step 2: set up neural network model; Described neural network model comprises four layers, and ground floor has 1 neuron, and the second layer has 1 neuron, and third layer has 6 neurons, and the 4th layer has 1 neuron; The neuron of ground floor is month sequence, and the neuron of the second layer is network parameter initial value, and third layer 6 neurons are the number of users in corresponding month in ground floor month sequence and corresponding 5 indexs, and the 4th layer be the number of users of the prediction of output; Wherein in the first level, to month modeling time series, month sequence data is converted into the differential equation;
Step 3: training and testing is carried out to neural network model;
Step 4: utilize the number of users being predicted districts and cities by the neural network model of test, wherein profit first inputs between detection data normalization to 0 and+1 again, and obtains the predicted value of number of users after the output valve after network operations is carried out renormalization;
Step 5: obtain user preference information, user preference information is uploaded to information server with character string forms;
Step 6: user preference information is excavated, comprise: calculate the text relevant eigenwert of the first character string and the second character string and the semantic dependency eigenwert of the first character string and the second character string, then with logic-based regression model, described text relevant eigenwert and semantic dependency eigenwert are fitted to the correlative character value of the first character string and the second character string;
Step 7: the Output rusults according to step 6 obtains potential user's quantity, exports market forecast number of users value after merging with step 4 Output rusults.
2. the product market share analytic system based on neural network, it is characterized in that being provided with netscape messaging server Netscape and plural user terminal, wherein netscape messaging server Netscape is connected with plural user terminal respectively, be provided with existing user in described netscape messaging server Netscape and estimate unit and potential user estimates unit, described user terminal is provided with receives information history data store unit, data screening unit, data upload/download unit, wherein storage terminal user ' s preference in receives information history data store unit.
3. a kind of product market share analytic system based on neural network according to claim 2, the existing user that it is characterized in that in described netscape messaging server Netscape estimates unit and comprises data acquisition module, data preprocessing module, data-mining module and data outputting module, described data acquisition module is for obtaining the number of users in a period of time of somewhere, and for influential 5 indexs of number of users in business, comprising: market share, product popularity rate, monthly festivals or holidays number of days, disposal income of Chinese people and resident population; Described data preprocessing module is used for being normalized the data of data acquisition module collection.
4. a kind of product market share analytic system based on neural network according to claim 2, it is characterized in that described potential user estimates unit and comprises character string receiving element, correlative character value computing unit and correlative character value fitting unit, wherein: character string receiving element, for receiving the first character string and the second character string; Correlative character value computing unit, for the semantic dependency eigenwert of the text relevant eigenwert and the first character string and the second character string that calculate the first character string and the second character string; Correlative character value fitting unit, fits to the correlative character value of the first character string and the second character string by described text relevant eigenwert and semantic dependency eigenwert for logic-based regression model.
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