In view of the slow convergence speed, difficulty of escaping from the local optimum, and difficulty maintaining the stability associated with the basic whale optimization algorithm (WOA), an improved WOA algorithm (REWOA) is proposed based on dual-operation strategy collaboration. Firstly, different evolutionary strategies are integrated into different dimensions of the algorithm structure to improve the convergence accuracy and the randomization operation of the random Gaussian distribution is used to increase the diversity of the population. Secondly, special reinforcements are made to the process involving whales searching for prey to enhance their exclusive exploration or exploitation capabilities, and a new skip step factor is proposed to enhance the optimizer’s ability to escape the local optimum. Finally, an adaptive weight factor is added to improve the stability of the algorithm and maintain a balance between exploration and exploitation. The effectiveness and feasibility of the proposed REWOA are verified with the benchmark functions and different experiments related to the identification of the Hammerstein model.
This paper focuses on the problem that the current path planning algorithm is not mature enough to achieve the expected goal in a complex dynamic environment. In light of the ant colony optimization (ACO) with good robustness and strong search ability, and the dynamic window method (DWA) with better planning effect in local path planning problems, we propose a fusion algorithm named DACO that can quickly and safely reach the designated target area in a complex dynamic environment. This paper first improves the ant colony optimization, which greatly improves the convergence performance of the algorithm and shortens the global path length. On this basis, we propose a second-level safety distance determination rule to deal with the special problem of the research object encountering obstacles with unknown motion rules, in order to perfect the obstacle avoidance function of the fusion algorithm in complex environments. Finally, we carry out simulation experiments through MATLAB, and at the same time conduct three-dimensional simulation of algorithm functions again on the GAZEBO platform. It is verified that the algorithm proposed in this paper has good performance advantages in path planning and dynamic obstacle avoidance.INDEX TERMS Motion planning, path planning, ant colony optimization, dynamic window approach, mobile robot.
Collaborative filtering has become one of the most widely used methods for a variety of commercial recommendations. The key to collaborative filtering is use similarity calculation formula to find similar neighbors or projects. However, most similarity calculation methods only use the user common score and provide bad recommendations. This paper proposes a new similarity measure method, which effectively utilizes the user context information. The new method uses a singularity factor to adjust nonlinear equation and takes into account the user scoring habits. It can improve the accuracy of the prediction. The new method has been tested on the dataset and compared with other algorithms. The results show that the proposed method can improve the recommendation quality. INDEX TERMS Recommender system, collaborative filtering, context information.
In the current paper, a hybrid depth neural network model, TBLC-rAttention, aiming at Chinese text emotion recognition, is proposed to identify the emotional tendency of the Chinese medical reviews. The model includes the following steps: acquiring and preprocessing the Chinese corpus; mapping the preprocessed text into the word vectors; using Bi-directional Long Short-Term Memory network (Bi-LSTM) with the attention mechanism to acquire the context semantic features of the text; using Convolutional Neural Network (CNN) to obtain local semantics features on the basis of the context semantic features; and inputting the final feature vectors into the classification layer to complete the task of emotion recognition and the classification of the Chinese medical reviews. In this experiment, the corpus data is the comments of 999 cold medicine on a large e-commerce platform. All corpus are divided into three types, including high praise, medium praise and bad review. Classical machine learning models (SVM, NB) and neural network models (CNN, LSTM, Bi-LSTM, BiLSTM-Attention and RCNN) are performed as the comparison benchmarks to assess the category performance of TBLC-rAttention model. All the results were obtained when the training accuracy and test accuracy were stable after 1000 cycles of repeated calculation. The results show that TBLC-rAttention can get better text feature than the reference models, and the text classification accuracy reaches to 99%. In conclusion, the TBLC-rAttention model can identify semantic feature information to the greatest extent. In addition, this study also completes the numerical quantification of the predicted results.INDEX TERMS Attention mechanism, bi-directional long short-term memory network (Bi-LSTM), Chinese medical comment, Chinese text emotion recognition, convolutional neural network (CNN), deep learning, feature extraction, hybrid neural network, naive bayes (NB), natural language processing (NLP), support vector machine (SVM).
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