Performance evaluation of enterprises’ innovation capacity based on fuzzy system model and convolutional neural network

2020 ◽  
Vol 39 (2) ◽  
pp. 1563-1571
Author(s):  
Abuduaini Abudureheman ◽  
Aishanjiang Nilupaer ◽  
Yi He
Keyword(s):  
Neural Network ◽  
Performance Evaluation ◽  
Convolutional Neural Network ◽  
Fuzzy System ◽  
Evaluation Method ◽  
Innovation Performance ◽  
System Model ◽  
Innovation Capacity ◽  
Performance Evaluation Index ◽  
Breakthrough Innovation

Influenced by national policies and macro-economic environment, large domestic enterprises is actively promoting strategic transformation to enhance their core competitiveness, and performance evaluation of enterprises’ innovation capacity has become a hot topic in recent years. This paper proposes a performance evaluation method of enterprises’ innovation capacity based on deep learning fuzzy system model and convolutional neural network analysis of innovation network. First of all, on account of the characteristics of breakthrough innovation and drawing on the traditional innovation performance evaluation model, this paper constructs a breakthrough innovation performance evaluation index system for enterprises from the six dimensions of main resource input, technology out-turn, process management, product performance, social value and commercial Value. Secondly, the introduction of machine learning of fuzzy convolutional neural network to assess the advancement execution of enterprises is of great significance for enterprise managers to find out the problems and causes of enterprises’ innovation, optimize the allocation of enterprises’ resources and further improve the innovation performance of enterprises. The experimental results show to verify the adequacy of the algorithm.

scholarly journals Cement pavement performance evaluation based on the discrete Hopfield neural network

2019 ◽  
Vol 136 ◽  
pp. 04069
Author(s):  
Huan Liu ◽  
Peng Liu ◽  
Qiuyu Peng
Keyword(s):  
Neural Network ◽  
Performance Evaluation ◽  
Evaluation Method ◽  
Functional Performance ◽  
Hopfield Neural Network ◽  
Evaluation Index ◽  
Pavement Performance ◽  
Index Matrix ◽  
Performance Evaluation Index ◽  
Grading Standards

Because of the deficiency of the index of cement pavement performance evaluation and the defect of the evaluation method in the specification, the performance of the pavement is comprehensively evaluated by seven optimized indexes and grading standards that reflect functional performance and structure of the pavement. Because the discrete Hopfield neural network is available with simple construction procedure, less training samples, and strong objectivity.The DHNN is constructed by MATLAB to evaluate the performance of test pavement. The ideal cement pavement performance grading evaluation index matrix and 6 places unclassified of test pavement performance evaluation index matrix are input to the neural network then the evaluation result is obtained after simulating and learning. Finally, comparing the result of the DHNN with the fuzzy complex matter element method and the nonlinear fuzzy method, it is proved that the discrete Hopfield neural network evaluation method is reliable.

scholarly journals Model and algorithm of innovation performance evaluation for coordination of supply and demand based on wireless sensor network

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Ye Zhao ◽  
Beiwei Li
Keyword(s):  
Performance Evaluation ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Evaluation Method ◽  
Evaluation Model ◽  
Supply And Demand ◽  
Innovation Performance ◽  
Wireless Sensor ◽  
Performance Evaluation Model ◽  
Performance Evaluation Index

AbstractIn recent years, with the continuous development and progress of wireless communication and sensors, people's production and life have also undergone tremendous changes. This article aims to apply wireless sensor networks to the construction of a supply–demand coordination innovation performance evaluation model, in order to improve its influence and application scope in real life. This paper deeply researches the architecture and node organization structure of wireless sensor network, and strengthens its theoretical foundation in the application of performance evaluation model. This paper designs performance evaluation indicators and compares performance evaluation methods at home and abroad based on the evaluation indicators, compares and analyzes the factor analysis method, fuzzy comprehensive evaluation method, comprehensive index evaluation method, etc., draws the advantages and disadvantages of each method, and uses them reasonably. The performance evaluation model constructed in this paper adopts the production function method and the analytic hierarchy process. According to the principles of scientificity, feasibility, and economy, the performance indicators for evaluating the balance of supply and demand are screened out, and score evaluation and comparison of each indicator are carried out. Finally, this paper analyzes the corporate performance evaluation index composition, model regression, sensor performance and performance evaluation scores, etc., and has a comprehensive application analysis of the model constructed in this paper. As can be seen from the overall score we selected five companies, enterprises composite score is 83.574, ranking first, followed by a score of 78.421.

Analysis of Performance Evaluation Index for bridge group networking monitoring and collaborative supervision

2021 ◽  
Author(s):  
Liye Zhang ◽  
Yong He ◽  
Shoushan Cheng ◽  
Guoliang Wang ◽  
Hongwei Ren ◽  
...  
Keyword(s):  
Performance Evaluation ◽  
Evaluation Method ◽  
Field Testing ◽  
Technical Condition ◽  
Evaluation Index ◽  
Technical Problems ◽  
Condition Evaluation ◽  
Bridge Group ◽  
Performance Evaluation Index ◽  
And Performance

<p>With the number of bridges increases, the bridge health monitoring (BHM) technique is developing from single bridge monitoring to collaborative supervision of bridge group. Therefore, there are many technical problems need to be solved especially the performance evaluation index for bridge group network. This paper analyses the performance evaluation index of the bridges and bridge group network, establishes the performance evaluation index for bridge group based on rating factor (RF) and technical condition evaluation index. Based on bridge field testing and monitoring data, bridge technical condition evaluation index and performance evaluation method for bridge group are proposed. A case study demonstrates that the research results provide support for bridge group networking monitoring and collaborative supervision.</p>

scholarly journals Performance Evaluation of Fine-tuned Faster R-CNN on specific MS COCO Objects

2019 ◽  
Vol 9 (4) ◽  
pp. 2548
Author(s):  
Garima Devnani ◽  
Ayush Jaiswal ◽  
Roshni John ◽  
Rajat Chaurasia ◽  
Neha Tirpude
Keyword(s):  
Neural Network ◽  
Performance Evaluation ◽  
Convolutional Neural Network ◽  
Training Model ◽  
Fine Tuning ◽  
Training Process

<span lang="EN-US">Fine-tuning of a model is a method that is most often required to cater to the users’ explicit requirements. But the question remains whether the model is accurate enough to be used for a certain application. This paper strives to present the metrics used for performance evaluation of a Convolutional Neural Network (CNN) model. The evaluation is based on the training process which provides us with intermediate models after every 1000 iterations. While 1000 iterations are not substantial enough over the range of 490k iterations, the groups are sized with 100k iterations each. Now, the intention was to compare the recorded metrics to evaluate the model in terms of accuracy. The training model used the set of specific categories chosen from the Microsoft Common Objects in Context (MS COCO) dataset while allowing the users to use their externally available images to test the model’s accuracy. Our trained model ensured that all the objects are detected that are present in the image to depict the effect of precision.</span>

Deep Convolutional Neural Network driven Neuro-Fuzzy System for Moving Target Detection Using the Radar Signals

2021 ◽  
Author(s):  
M. Bharat Kumar ◽  
P. Rajesh Kumar
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Target Detection ◽  
Fuzzy System ◽  
Deep Convolutional Neural Network ◽  
Moving Target ◽  
Moving Targets ◽  
Moving Target Detection ◽  
Neuro Fuzzy ◽  
Deep Cnn

In radar signal processing, detecting the moving targets in a cluttered background remains a challenging task due to the moving out and entry of targets, which is highly unpredictable. In addition, detection of targets and estimation of the parameters have become a major constraint due to the lack of required information. However, the appropriate location of the targets cannot be detected using the existing techniques. To overcome such issues, this paper presents a developed Deep Convolutional Neural Network-enabled Neuro-Fuzzy System (Deep CNN-enabled Neuro-Fuzzy system) for detecting the moving targets using the radar signals. Initially, the received signal is presented to the Short-Time Fourier Transform (STFT), matched filter, radar signatures-enabled Deep Recurrent Neural Network (Deep RNN), and introduced deep CNN to locate the targets. The target location output results are integrated using the newly introduced neuro-fuzzy system to detect the moving targets effectively. The proposed deep CNN-based neuro-fuzzy system obtained effective moving target detection results by varying the number of targets, iterations, and the pulse repetition level for the metrics, like detection time, missed target rate, and MSE with the minimal values of 1.221s, 0.022, and 1,952.15.

Research on Innovation Performance Evaluation Index System of the Productive Service Industry Cluster Based on the Process of Network Innovation

2013 ◽  
Vol 869-870 ◽  
pp. 856-859
Author(s):  
Liang Hu Xu ◽  
Ming Cao ◽  
Qing Miao
Keyword(s):  
Performance Evaluation ◽  
Index System ◽  
Service Industry ◽  
Industrial Clusters ◽  
Innovation Performance ◽  
Evaluation Index System ◽  
Evaluation Index ◽  
Industry Cluster ◽  
Performance Evaluation Index ◽  
Performance Evaluation Index System

In this paper, from the perspective of network innovative process, we comes up with the basic assumption, that is, the network innovation can promote absorptive capacity of industrial clusters products so as to improve the innovation performance of the enterprises in cluster. On the basis of this, this paper adopts correlation analysis method to verify the influence extent of productive service industry cluster innovation network on innovation performance. Therefore, we find critical influencing factors of network nodes and set up the innovation performance evaluation index system of productive service industry cluster.

Identifying Emotion on Indonesian Tweets using Convolutional Neural Networks

2021 ◽  
Vol 5 (3) ◽  
pp. 584-593
Author(s):  
Naufal Hilmiaji ◽  
Kemas Muslim Lhaksmana ◽  
Mahendra Dwifebri Purbolaksono
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Performance Evaluation ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Text Classification ◽  
Classification Model ◽  
Learning Methods ◽  
Expected Performance

especially with the advancement of deep learning methods for text classification. Despite some effort to identify emotion on Indonesian tweets, its performance evaluation results have not achieved acceptable numbers. To solve this problem, this paper implements a classification model using a convolutional neural network (CNN), which has demonstrated expected performance in text classification. To easily compare with the previous research, this classification is performed on the same dataset, which consists of 4,403 tweets in Indonesian that were labeled using five different emotion classes: anger, fear, joy, love, and sadness. The performance evaluation results achieve the precision, recall, and F1-score at respectively 90.1%, 90.3%, and 90.2%, while the highest accuracy achieves 89.8%. These results outperform previous research that classifies the same classification on the same dataset.

Sign in / Sign up

Export Citation Format

Share Document