摘要：The paper presents a method for calculating the preferred approach when evaluating intellectual property, taking into account local confidence factors, which makes it possible to obtain the final value of intellectual property with high probability and accuracy, using three wellknown approaches. This method has a high practical significance, since all evaluators have to constantly face the choice of the preferred approach. Local confidence factors have been developed with the respect to the type of intellectual property, the type of production, the stage of the life cycle, the purpose of using the object, the value of the indicator deviation from the average value. The influencing factor determines the local confidence factor for each method of evaluation separately through the established functional dependence. The method used neural network modeling, which allows quickly solving a whole range of problems in a single method. Using the proposed method, it is possible to automatically calculate the weighting coefficients of the income, cost, and comparative method, ensuring the necessary accuracy of the final value.
其他摘要：The paper presents a method for calculating the preferred approach when evaluating intellectual property, taking into account local confidence factors, which makes it possible to obtain the final value of intellectual property with high probability and accuracy, using three wellknown approaches. This method has a high practical significance, since all evaluators have to constantly face the choice of the preferred approach. Local confidence factors have been developed with the respect to the type of intellectual property, the type of production, the stage of the life cycle, the purpose of using the object, the value of the indicator deviation from the average value. The influencing factor determines the local confidence factor for each method of evaluation separately through the established functional dependence. The method used neural network modeling, which allows quickly solving a whole range of problems in a single method. Using the proposed method, it is possible to automatically calculate the weighting coefficients of the income, cost, and comparative method, ensuring the necessary accuracy of the final value.
