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With the increasing number of vehicles in China, traffic condition analysis is of great significance to urban planning and public administration. However, the state-of-the-art traffic condition analysis approaches mainly rely on sensors, which are high cost and limited coverage. To solve these problems, we propose a semi-supervised learning method which uses the social network data instead and analyzes the traffic condition based on users’ sentiment in Chinese Microblog. This approach is a low-cost crowdsourcing solution. Firstly, we train the gated recurrent unit (GRU) model and generative adversarial networks to estimate the sentiment of Microblog with traffic information. Secondly, we calculate the Traffic Sentiment Index to predict whether traffic jams happen or not. In order to reduce the data annotated by manpower, we propose a new idea to employ the conditional generative adversarial networks to generate robust features which are used as a supplement to the training set of GRU. Finally compared with the GRU model trained by solely the manual annotation data, our method improves the classification accuracy by 3.79%. Furthermore, by using the Traffic Sentiment Index, we build a traffic condition analysis system and predict the time and roads of traffic jams in 4 Chinese cities. The predication experiment shows similar results with Baidu map which uses a lot of mobile phones as sensors, and proves the low-cost characteristic and performance of our approach. [ABSTRACT FROM AUTHOR]

Since classical public sentiment analysis systems for microblog are based on the text sentiment analysis, it is difficult to determine the sentiment of short text without clear sentiment words in microblog posts. Fortunately, a lot of microblog posts contain images which also represent users' sentiment. To fully understand users' sentiment, we propose a cross-media public sentiment analysis system for microblog. The best advantage of this novel system is the unified cross-media public sentiment analysis framework which fuses the text sentiment and image sentiment not only from sentiment results, but also from sentiment ontology. To enhance presentation effects, this system presents sentiment results from macroscopic view and microscopic view which details the sentiment results in region, topic, microblog content and user diffusion. In our knowledge, this is the first unified cross-media public sentiment analysis system. [ABSTRACT FROM AUTHOR]

Fuzzy cerebellar model articulation controller (FCMAC) networks with excellent nonlinear appropriation ability and simple implementation are used to solve complex uncertainties problems in engineering applications. Both online and off-line learning algorithm of FCMAC networks usually applies the gradient-descent-type methods. However, such gradient-descent methods lead to the high possibility to converging into local minima. To cope with the local minimum problem, this paper alternatively proposes to apply harmony search algorithm to find optimal network parameters, so as to achieve better performances of FCMAC. The harmony search algorithm optimizes not only FCMAC network’s weight variables, but also optimizes network receptive field’s center position and standard deviation parameters. In order to obtain an optimal network, the weight values, center positions, and standard deviations are transformed to three data strings that can be processed by harmony search algorithm. In particular, the self-adaptive global best harmony search algorithm (SGHS) is used to search optimal parameter combinations of FCMAC within solution domains. The network’s performances are verified by approximating six nonlinear formulae. In order to compare the performances of the FCMAC networks optimized by the SGHS algorithm, a back-propagation trained network and another harmony search variant optimized networks are also tested in this work. The experimental results show that the networks optimized by SGHS perform the faster convergence speed and better accuracy. [ABSTRACT FROM AUTHOR]