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Fuzzy system is a kind of artificial intelligence method with strong explanatory ability.The classical Wang-Mendel (WM) method can automatically obtain fuzzy rules from data,and it has become an important intelligent modeling method.There are many problems in this method,such as the large number of rules and the low precision.And at present,there are many problems in the improved methods,such as complex calculation and low efficiency.For this reason,a new method of fuzzy system optimization modeling based on improved genetic algorithm and rule reduction based on support degree:genetic fuzzy system (GFS) was proposed.By optimizing the structure and membership function parameters of the fuzzy system,the concrete algorithm of GFS1,GFS2 and GFS3 models were constructed by different combinations of objective functions.The results of fuzzy modeling experiments on the standard and noisy power output data sets show that:1) GFSi (i =1,2,3) model fitting accuracy is higher than WM method and the number of rules is less; 2) its anti-noise capability is significantly better than that of RBF and BP neural network; 3) the fitness function of GFS3 has the best evaluation effect,so its performance is optimal.The method proposed in this paper gives full play to the advantages of the interpretability and robustness of the fuzzy system and guarantees the accuracy at the same time.It is a potential artificial intelligence algorithm.

The industrial Internet of things makes full use of sensors,computer networks,big data process and other technologies to solve the problem of “information island” caused by the closed operation of traditional industrial systems,enabling the rapid and convenient data exchange between systems.How to establish high quality and low bandwidth data exchange mode in industrial field and cloud is a big challenge to realize intelligent manufacturing.MQTT protocol is widely used in the Internet of things system because of its lightweight and flexible configuration.MQTT-based industrial Internet of things communication system was built to realize multi-source and heterogeneous data exchange between cloud and industrial field,remote monitoring of device status,user service subscription and other functions,so that the performance and related applications of MQTT protocol in industrial Internet of things communication system could be studied.

The concept,architecture,methods and applications of parallel transportation systems were introduced.The main idea is to build software defined artificial transportation systems for specific traffic application tasks and scenarios;use computational experimental methods for experiments,analysis,evaluation,prediction,learning,and optimization;and parallel execution is conducted for management and control of urban transportation systems,leading to virtual-real interactive parallel intelligence.Parallel traffic management and control is an extension and application of ACP approach,which emphasizes using virtual-real interactive ways to generate parallel intelligence for urban transportation systems,towards improving the intelligence of urban traffic management and control.

The effective processing and deep mining analysis of data can provide manufacturers with more effective production scheduling,equipment management and other strategies to affect production yield and efficiency.The processing methods and technologies of multi-source heterogeneous data in the manufacturing process were systematically reviewed.Firstly,the multi-source heterogeneous data content and classification in the manufacturing process was clarified.Secondly,the data processing methods and techniques applied in various stages of data collection,data integration and data analysis in the multi-source heterogeneous data processing were described.And the advantages and disadvantages of various techniques and their applications were analyzed.Finally,the multi-source heterogeneous data processing methods and techniques in the manufacturing process were summarized.And the challenges and development trends were pointed out.