با سلام خدمت کاربران عزیز، به اطلاع می رساند ترجمه مقالاتی که سال انتشار آن ها زیر 2008 می باشد رایگان بوده و میتوانید با وارد شدن در صفحه جزییات مقاله به رایگان ترجمه را دانلود نمایید.
Rapid discrimination of Salvia miltiorrhiza according to their geographical regions by laser induced breakdown spectroscopy (LIBS) and particle swarm optimization-kernel extreme learning machine (PSO-KELM)
تبعیض سریع miltiorrhiza مریم گلی با توجه به مناطق جغرافیایی خود را با طیف سنجی شکست ناشی از لیزر (LIBS) و یادگیری ماشین افراطی بهینه سازی ازدحام ذرات (PSO-KELM)-2020
Laser-induced breakdown spectroscopy (LIBS) coupled with particle swarm optimization-kernel extreme learning machine (PSO-KELM) method was developed for classification and identification of six types Salvia miltiorrhiza samples in different regions. The spectral data of 15 Salvia miltiorrhiza samples were collected by LIBS spectrometer. An unsupervised classification model based on principal components analysis (PCA) was employed first for the classification of Salvia miltiorrhiza in different regions. The results showed that only Salvia miltiorrhiza samples from Gansu and Sichuan Province can be easily distinguished, and the samples in other regions present a bigger challenge in classification based on PCA. A supervised classification model based on KELM was then developed for the classification of Salvia miltiorrhiza, and two methods of random forest (RF) and PSO were used as the variable selection method to eliminate useless information and improve classification ability of the KELM model. The results showed that PSO-KELM model has a better classification result with a classification accuracy of 94.87%. Comparing the results with that obtained by particle swarm optimization-least squares support vector machines (PSO-LSSVM) and PSO-RF model, the PSO-KELM model possess the best classification performance. The overall results demonstrate that LIBS technique combined with PSO-KELM method would be a promising method for classification and identification of Salvia miltiorrhiza samples in different regions.
Keywords: Laser-induced breakdown spectroscopy | Particle swarm optimization | Kernel extreme learning machine | Salvia miltiorrhiza | Classification
An echo state network architecture based on quantum logic gate and its optimization
معماری شبکه ای حالت اکو مبتنی بر دروازه منطق کوانتومی و بهینه سازی آن-2020
Quantum neural network (QNN) is developed based on two classical theories of quantum computation and artificial neural networks. It has been proved that quantum computing is an important candidate for improving the performance of traditional neural networks. In this work, inspired by the QNN, the quantum computation method is combined with the echo state networks (ESNs), and a hybrid model namely quantum echo state network (QESN) is proposed. Firstly, the input training data is converted to quantum state, and the internal neurons in the dynamic reservoir of ESN are replaced by qubit neurons. Then in order to maintain the stability of QESN, the particle swarm optimization (PSO) is applied to the model for the parameter optimizations. The synthetic time series and real financial application datasets (Standard & Poor’s 500 index and foreign exchange) are used for performance evaluations, where the ESN, autoregressive integrated moving average (ARIMAX) are used as the benchmarks. Results show that the proposed PSO-QESN model achieves a good performance for the time series predication tasks and is better than the benchmarking algorithms. Thus, it is feasible to apply quantum computing to the ESN model, which provides a novel method to improve the ESN performance.
Keywords: Quantum computation | Echo state network | Particle swarm optimization | Time series | Financial applications
A closed-loop brain–machine interface framework design for motorrehabilitation
طراحی چارچوب رابط مغز و ماشین با حلقه بسته برای توان بخشی در موتور-2020
Brain–machine interfaces (BMIs) can be adopted to rehabilitate motor systems for disabled subjectsby sensing cortical neuronal activities and creating new method. In this paper, to achieve the functionof motor rehabilitation, two generalized BMI frameworks, including decoders, encoders and auxiliarycontrollers, are proposed and compared based on a classical single-joint information transmission model.Firstly, a decoder based on the Wiener filter and an encoder based on a network of spiking neuronsare designed to compensate for the absent information pathway, and a charge-balanced intra-corticalmicrostimulation current is chosen as the input of the spiking neuron network; Secondly, to formulateclosed-loop BMI frameworks, two auxiliary controllers are designed according to the strategy of modelpredictive control, where the controller inputs are the position of joint muscle trajectories and the averagefiring activity trajectories of perceived position vector neurons. Thirdly, considering that several integerparameters are included in the charge-balanced intra-cortical microstimulation current and that theoptimization problem for solving the control inputs also includes these decision variables, a particleswarm optimization algorithm is adopted to solve the hard optimization problem. We compare the motorrecovery effectiveness of the two presented frameworks through these simulations and choose the betterframework for future BMI system design. The proposed frameworks provide a important theoreticalguidance for designing BMI system applied in future life
Keywords:Brain–machine interface | Framework design | Auxiliary controller | Network of spiking neurons | Particle swarm optimization
A non-canonical hybrid metaheuristic approach to adaptive data stream classification
یک روش متاوریستی ترکیبی غیر متعارف برای طبقه بندی جریان داده تطبیقی-2020
Data stream classification techniques have been playing an important role in big data analytics recently due to their diverse applications (e.g. fraud and intrusion detection, forecasting and healthcare monitoring systems) and the growing number of real-world data stream generators (e.g. IoT devices and sensors, websites and social network feeds). Streaming data is often prone to evolution over time. In this context, the main challenge for computational models is to adapt to changes, known as concept drifts, using data mining and optimisation techniques. We present a novel ensemble technique called RED-PSO that seamlessly adapts to different concept drifts in non-stationary data stream classification tasks. RED-PSO is based on a three-layer architecture to produce classification types of different size, each created by randomly selecting a certain percentage of features from a pool of features of the target data stream. An evolutionary algorithm, namely, Replicator Dynamics (RD), is used to seamlessly adapt to different concept drifts; it allows good performing types to grow and poor performing ones to shrink in size. In addition, the selected feature combinations in all classification types are optimised using a non-canonical version of the Particle Swarm Optimisation (PSO) technique for each layer individually. PSO allows the types in each layer to go towards local (within the same type) and global (in all types) optimums with a specified velocity. A set of experiments are conducted to compare the performance of the proposed method to state-of-the-art algorithms using real-world and synthetic data streams in immediate and delayed prequential evaluation settings. The results show a favourable performance of our method in different environments.
Keywords: Ensemble learning | Data stream mining | Concept drifts | Bio-inspired algorithms | Non-stationary environments | Particle swarm optimisation | Replicator dynamics
Deep Learning-Driven Particle Swarm Optimisation for Additive Manufacturing Energy Optimisation
بهینه سازی ازدحام ذرات با محوریت یادگیری عمیق برای بهینه سازی انرژی تولید افزودنی-2019
The additive manufacturing (AM) process is characterised as a high energy-consuming process, which has a significant impact on the environment and sustainability. The topic of AM energy consumption modelling, prediction, and optimisation has then become a research focus in both industry and academia. This issue involves many relevant features, such as material condition, process operation, part and process design, working environment, and so on. While existing studies reveal that AM energy consumption modelling largely depends on the design-relevant features in practice, it has not been given sufficient attention. Therefore, in this study, design-relevant features are firstly examined with respect to energy modelling. These features are typically determined by part designers and process operators before production. The AM energy consumption knowledge, hidden in the design-relevant features, is exploited for prediction modelling through a design-relevant data analytics approach. Based on the new modelling approach, a novel deep learning-driven particle swarm optimisation (DLD-PSO) method is proposed to optimise the energy utility. Deep learning is introduced to address several issues, in terms of increasing the search speed and enhancing the global best of PSO. Finally, using the design-relevant data collected from a real-world AM system in production, a case study is presented to validate the proposed modelling approach, and the results reveal its merits. Meanwhile, optimisation has also been carried out to guide part designers and process operators to revise their designs and decisions in order to reduce the energy consumption of the designated AM system under study.
Keywords: Additive Manufacturing | Energy Consumption Modelling | Prediction and Optimisation | Deep Learning | Particle Swarm Optimisation
Analysis of earnings forecast of blockchain financial products based on particle swarm optimization
تحلیل پیش بینی درآمد محصولات مالی بلاکچین بر اساس بهینه سازی ازدحام ذرات-2019
The purpose of this study is to solve the problems of large number of iterations, limitations and poor fitting effect of traditional algorithms in predicting the yield rate of blockchain financial products. In this study, bitcoin yield rate is taken as the research object, and data from June 2, 2016 to December 30, 2018 are collected, totaling 943 pieces. The BP neural network, support vector regression machine algorithm and particle swarm optimization least square vector algorithm are respectively adopted to carry out model simulation and empirical analysis on the collected data, and it is concluded that particle swarm optimization least square vector algorithm has the best fitting effect. Subsequently, the Ethereum (ETH) yield rate is selected as the research object, and the model simulation and empirical analysis are carried out on it, which verifies that the optimized algorithm has better prediction and fitting on the time series. The results show that the particle swarm optimization algorithm among the three algorithms mentioned in this research has the best prediction effect. Therefore, the results of this study have a good fitting effect on the prediction of the yield rate of blockchain financial products, have a good guiding effect on the investors of blockchain financial products, and have a good guiding significance for the study of the yield rate of China’s blockchain financial products.
Keywords: Particle swarm optimization | Blockchain | Financial product | Earnings
Study of LED array fill light based on parallel particle swarm optimization in greenhouse planting
مطالعه نور پر کننده آرایه LED بر اساس بهینه سازی ازدحام ذرات موازی در کاشت گلخانه ای-2019
Agricultural productivity is crucial to the economy. The output and quality of crops have a direct impact on people’s daily lives and a country’s food and clothing. Therefore, harvesting high-quality crops efficiently and maximizing yield per unit area are the most important goals pursued by farmers. As an important parameter of plant growth, light intensity is one of the important factors that affects plant growth and development, morphological establishment and accumulation of functional chemical substances. When light intensity cannot meet the plant’s needs, the optimal light intensity or uneven light distribution will have a greater impact on plant growth and development. This paper aims to address the optimal plant light intensity problem. The paper presents an expert system technology database storing the empirical value of real-time light intensity values and compares it with a parallel particle swarm optimization algorithm to identify the optimal locations where LED lights need to be turned on and where drive circuit lit LED arrays need to be situated, to identify the number of LED fill lights and to solve light intensity optimization problems.
Keywords: Particle swarm optimization | Greenhouse | Fill light | Expert system | LED
Swarm intelligence techniques in recommender systems - A review of recent research
تکنیک های هوش ازدحام در سیستم های توصیه کننده - مروری بر تحقیقات اخیر-2019
One of the main current applications of Intelligent Systems are Recommender systems (RS). RS can help users to find relevant items in huge information spaces in a personalized way. Several techniques have been investigated for the development of RS. One of them are Swarm Intelligence (SI) techniques, which are an emerging trend with various application areas. Although the interest in using Computational Intelligence in web personalization and information retrieval fostered the publication of some survey papers, these surveys so far focused on different application domains, e.g., clustering, or were too broadly focused and incorporated only a handful of SI approaches. This study provides a comprehensive review of 77 research publications applying SI in RS. The study focus on five aspects we consider relevant for such: the recommendation technique used, the datasets and the evaluation methods adopted in their experimental parts, the baselines employed in the experimental comparison of proposed approaches and the reproducibility of the reported results. At the end of this review, we discuss negative and positive aspects of these papers, as well as point out opportunities, challenges and possible future research directions. To the best of our knowledge, this survey is the most comprehensive review of approaches using SI in RS. Therefore, we believe this review will be a relevant material for researchers interested in either of the domains.
Keywords: Swarm intelligence | Particle swarm optimization | Ant colony optimization | Invasive weed optimization | Artificial bee colony | Recommender systems | Personalization
Pattern recognition of SEMG based on wavelet packet transform and improved SVM
تشخیص الگوی SEMG بر اساس تبدیل بسته های موجک و بهبود SVM-2019
The purpose of this paper is to solve the problem of low recognition accuracy of three-degree-offreedom myoelectric prosthesis and long training time.According to the nonstationarity of the EMG signal, the wavelet packet is used to decompose the EMG signal and the energy and variance of the wavelet packet coefficients of the four-channel EMG signal are extracted as feature vectors.Then Particle Swarm Optimization(PSO) was combined with improved support vector machine(ISVM) to construct a new model(PSO-ISVM). Under the premise of ensuring the sparseness of the SVM, the slack variables and the decision function was improved to reduce the constraint conditions for solving the optimal face in the quadratic programming. SVM is optimized by the PSO in order to improve the accuracy of the model.The experimental results show that the improved algorithm can effectively identify six kinds of commonly used upper limb movements compared with the traditional SVM. The average recognition rate reaches 90.66% and training time can be shortened 0.042 s.
Keywords: Three degrees of freedom electromyographic | prosthesis | EMG | Wavelet packet | SVM | Particle swarm optimization
Clustering of multi-view relational data based on particle swarm optimization
خوشه بندی داده های رابطه ای چند منظوره بر اساس بهینه سازی ازدحام ذرات-2019
Clustering of multi-view data has received increasing attention since it explores multiple views of data sets aiming at improving clustering accuracy. Particle Swarm Optimization (PSO) is a well-known population-based meta-heuristic successfully used in cluster analysis. This paper introduces two hybrid clustering methods for multi-view relational data. These hybrid methods combine PSO and hard clus- tering algorithms based on multiple dissimilarity matrices. These methods take advantage of the global convergence ability of PSO and the local exploitation of hard clustering algorithms in the position up- date step, aiming to improve the balance between exploitation and exploration processes. Moreover, the paper provides adapted versions of 11 fitness functions suitable for vector data aiming at dealing with multi-view relational data. Two performance criteria were used to evaluate the clustering quality using the two proposed methods over eleven real-world data sets including image and document data sets. Among new findings, it was observed that the top three fitness functions are Silhouette index, Xu index and Intra-cluster homogeneity. The performance of the proposed algorithms was compared with previ- ous single and multi-view relational clustering algorithms. The results show that the proposed methods significantly outperformed the other algorithms in the majority of cases. The results reinforce the im- portance of the application of techniques such as PSO-based clustering algorithms in the field of expert systems and machine learning. Such application enhances classification accuracy and cluster compactness. Besides, the proposed algorithms can be useful tools in content-based image retrieval systems, providing good categorizations and automatically learning relevance weights for each cluster of images and sets of views.
Keywords: PSO | Cluster analysis | Multi-view clustering | Relational data