基于模糊认知图的语音情感识别研究

利用模糊认知图构建一种新的情感语音识别网络,该网络将权值矩阵分为两部分:一部分是特征与情感类别之间的权值,使用实数编码遗传算法学习;另一部分是情感类别与类别之间的权值,使用PAD三维情感模型学习。从数学和心理学两方面对模糊认知图的权值进行学习。针对国际公认的柏林情感语音库(EMO-DB)以及本实验室录制的情感语音库(TYUT)进行仿真实验,验证了方法的有效性,并与BP神经网络进行对比,实验结果表明该方法有较高的识别率。     

Learning fuzzy cognitive maps with required precision using genetic algorithm approach

Fuzzy cognitive maps (FCM) are a powerful and convenient tool for describing and analysing dynamic systems. Their generic design is performed manually, exploits expert knowledge and is quite tedious, especially in the case of larger systems. This shortcoming is alleviated by completing the design of FCMs through learning carried out on experimental data. Comprehensive experiments reveal that this approach helps design models of required accuracy in an automated manner.     

An integrated two-level hierarchical system for decision making in radiation therapy based on fuzzy cognitive maps

The radiation therapy decision-making is a complex process that has to take into consideration a variety of interrelated functions. Many fuzzy factors that must be considered in the calculation of the appropriate dose increase the complexity of the decision-making problem. A novel approach introduces fuzzy cognitive maps (FCMs) as the computational modeling method, which tackles the complexity and allows the analysis and simulation of the clinical radiation procedure. Specifically this approach is used to determine the success of radiation therapy process estimating the final dose delivered to the target volume, based on the soft computing technique of FCMs. Furthermore a two-level integrated hierarchical structure is proposed to supervise and evaluate the radiotherapy process prior to treatment execution. The supervisor determines the treatment variables of cancer therapy and the acceptance level of final radiation dose to the target volume. Two clinical case studies are used to test the proposed methodology and evaluate the simulation results. The usefulness of this two-level hierarchical structure discussed and future research directions are suggested for the clinical use of this methodology.     

Cooperative spectrum sensing in cognitive radio network using multiobjective evolutionary algorithms and fuzzy decision making

The cognitive radio has emerged as a potential solution to the problem of spectrum scarcity. Spectrum sensing unit in cognitive radio deals with the reliable detection of primary user’s signal. Cooperative spectrum sensing exploits the spatial diversity between cognitive radios to improve sensing accuracy. The selection of the weight assigned to each cognitive radio and the global decision threshold can be formulated as a constrained multiobjective optimization problem where probabilities of false alarm and detection are the two conflicting objectives. This paper uses evolutionary algorithms to solve this optimization problem in a multiobjective framework. The simulation results offered by different algorithms are assessed and compared using three performance metrics. This study shows that our approach which is based on the concept of cat swarm optimization outperforms other algorithms in terms of quality of nondominating solutions and efficient computation. A fuzzy logic based strategy is used to find out a compromise solution from the set of nondominated solutions. Different tests are carried out to assess the stability of the simulation results offered by the heuristic evolutionary algorithms. Finally the sensitivity analysis of different parameters is performed to demonstrate their impact on the overall performance of the system.     

改进可变模糊ISODATA模型在径流预测中的应用

在可变模糊ISODATA预测模型的基础上,引入最佳模糊集划分理论,提出了可变模糊ISODATA与最佳模糊聚类有机结合的预测模型。其基本思路是从年降水量、年平均气温、年平均相对湿度、相对日照时数和年蒸发量5个方面构建预报因子,利用拉格朗日约束函数确定传统可变模糊ISODATA模型的最佳聚类数,通过对变系数模型参数滤定和年径流量数值模拟,求取符合精度要求的类别变量特征值与预测对象之间的回归方程进行预测。在沱沱河站年径流量预报中,预测效果比传统模糊ISODATA模型进行预测的效果要好,在短期3a内年径流量模拟相对误差均在10%之内,确定性系数达到0.8。该方法拓宽了可变模糊ISODATA理论在影响因素不稳定条件下年径流量研究的应用范围,为径流量的短期科学预测提供了一种新方法。     

Fusion of fuzzy/neuro/evolutionary computing for knowledgeacquisition

Soft computing is a consortium of methodologies, which deal with various kinds of inaccuracies and uncertainties contained in real-world problems. In this paper soft computing is discussed from the viewpoint of a combination of characteristic features unique to fuzzy, neural, and evolutionary computing. This paper is intended to describe particularly the knowledge acquisition from inaccurate and uncertain data and to point out the areas where soft computing can become a breakthrough technology     

Evolutionary fuzzy neural networks for hybrid financial prediction

In this paper, an evolutionary fuzzy neural network using fuzzy logic, neural networks (NNs), and genetic algorithms (GAs) is proposed for financial prediction with hybrid input data sets from different financial domains. A new hybrid iterative evolutionary learning algorithm initializes all parameters and weights in the five-layer fuzzy NN, then uses GA to optimize these parameters, and finally applies the gradient descent learning algorithm to continue the optimization of the parameters. Importantly, GA and the gradient descent learning algorithm are used alternatively in an iterative manner to adjust the parameters until the error is less than the required value. Unlike traditional methods, we not only consider the data of the prediction factor, but also consider the hybrid factors related to the prediction factor. Bank prime loan rate, federal funds rate and discount rate are used as hybrid factors to predict future financial values. The simulation results indicate that hybrid iterative evolutionary learning combining both GA and the gradient descent learning algorithm is more powerful than the previous separate sequential training algorithm described in.     

CLUSTERING PROPERTIES OF FUZZY KOHONEN''''S SELF-ORGANIZING FEATURE MAPS

A new clustering algorithm called fuzzy self-organizing feature maps is introduced. It can process not only the exact digital inputs, but also the inexact or fuzzy non-digital inputs, such as natural language inputs. Simulation results show that the new algorithm is superior to original Kohonen's algorithm in clustering performance and learning rate.     

冕洞逐日运行图在地磁骚扰预报中的应用

着重对澳大利亚ＦＬＥＵＲＳ天文台太阳扇形射束东西扫描资料进行了分析,并据此采用计算机处理系统建立了起日面上冕洞位置逐日运行图。然后我们向冕洞逐日运行图输入了太阳活动低年的部分冕洞数据对冕洞变化的特必以及冕洞骚扰预报作出初步分析。     

A churn prediction model for prepaid customers in telecom using fuzzy classifiers

The incredible growth of telecom data and fierce competition among telecommunication operators for customer retention demand continues improvements, both strategically and analytically, in the current customer relationship management (CRM) systems. One of the key objectives of a typical CRM system is to classify and predict a group of potential churners form a large set of customers to devise profitable and targeted retention campaigns for keeping a long-term relationship with valued customers. For achieving the aforementioned objective, several churn prediction models have been proposed in the past for the accurate identification of the customers who are prone to churn. However, these previously proposed models suffer from a number of limitations which place strong barriers towards the direct applicability of such models for accurate prediction. Firstly, the feature selection methods adopted in majority of the past work neglected the information rich variables present in call details record for model development. Secondly, selection of important features was done through statistical methods only. Although statistical methods have been applied successfully in diverse domains, however, these methods alone without the augmentation of domain knowledge have the tendency to yield erroneous results. Thirdly, the previous models have been validated mainly with benchmark datasets which do not provide a true representation of real world telecom data consisting of noise and large number of missing values. Fourthly, the evaluation measures used in the past neglected the True Positive (TP) rate, which actually highlights the ability of a model to correctly classify the percentage of churners as compared to non-churners. Finally, the classifiers used in the previous models completely neglected the use of fuzzy classification methods which perform reasonably well for data sets with noise. In this paper, a fuzzy based churn prediction model has been proposed and validated using a real data from a telecom company in South Asia. A number of predominant classifiers namely, Neural Network, Linear regression, C4.5, SVM, AdaBoost, Gradient Boosting and Random Forest have been compared with fuzzy classifiers to highlight the superiority of fuzzy classifiers in predicting the accurate set of churners.     

WPANFIS: combine fuzzy neural network with multiresolution for network traffic prediction

A novel methodology for prediction of network traffic,WPANFIS,which relies on wavelet packet transform(WPT)for multi-resolution analysis and adaptive neuro-fuzzy inference system(ANFIS)is proposed in this article.The widespread existence of self-similarity in network traffic has been demonstrated in earlier studies,which exhibits both long range dependence(LRD)and short range dependence(SRD).Also,it has been shown that wavelet decomposition is an effective tool for LRD decorrelation.The new method uses WPT as extension of wavelet transform which can decoorrelate LRD and make more precisely partition in the high-frequency section of the original traffic.Then ANFIS which can extract useful information from the original traffic is implemented in this study for better prediction performance of each decomposed non-stationary wavelet coefficients.Simulation results show that the proposed WPANFIS can achieve high prediction accuracy in real network traffic environment.     

Use of fuzzy logic for modeling, estimation, and prediction inswitched reluctance motor drives

Switched reluctance motor drives may be used in many commercial applications due to their simplicity and low cost. These drives require rotor position feedback to operate. However, in many systems, rotor position sensors have disadvantages. In this paper, a position sensorless scheme is described which uses fuzzy modeling, estimation and prediction. An important feature is that saturation and real-time nonideal effects are not ignored, but that no mathematical model is required. Instead, a fuzzy logic-based model is constructed from both static and real-time motor data, and from this model the rotor position is estimated. The system also incorporates fuzzy logic-based methods to provide a high robustness against noise. This includes a fuzzy predictive filter which combines both fuzzy logic-based time-series prediction, as well as a heuristic knowledge-based algorithm to detect and discard feedback signal error. In addition, the method uses heuristic knowledge to choose the most desirable phase for angle estimation in order to minimize the effect of feedback error. It is also shown that, by using fuzzy logic, the estimation scheme offers a high robustness and reliability and is thus well suited to a wide range of systems     

认知无线电中基于支持向量机的频谱空闲度预测

研究认知无线系统中的频谱小时空闲度预测问题,针对GSM系统的载频小时空闲度时间序列的非线性特点,提出一种基于支持向量机的预测模型构建方法。为提高模型的预测精度,在GSM系统小时空闲度时间序列特征分析的基础上,利用序列的节假日特性和日周期特性,对数据序列进行了重构。仿真结果表明,与采用基于神经网络的预测模型相比,该预测方法对工作日和周末均具有较高的预测精度,其预测绝对百分比误差在4以内。     

模糊聚类和模糊模式识别在目标识别中的应用

提出了一种模糊聚类和模糊模式识别相结合的目标识别方法,并成功应用于海上舰船识别分类;同时引入聚类分析有效性评价的F统计量,实现了模糊聚类的自适应性,避免了聚类数目选取上存在的主观性.对于给定特征的海上舰船目标,仿真实现了对目标的聚类分析,获得目标的分类并形成标准模型库,并通过模糊模式识别对后继获得的目标特征样本在标准模型库中进行匹配,应用最大贴近度原则完成目标识别.仿真结果表明:对于复杂的战场环境,两种方法的结合是可行和有效的,可以满足战时实时性和准确性的要求,具有一定的应用前景.     

模糊查询在电子商务数据库查询中的应用

为了在当前的电子商务关系型数据库上实现数值上的模糊查询,将模糊逻辑理论引入数据库的查询中,分析数据并建立隶属函数.从而使关系型电子商务数据库在经过调整后支持数值上的模糊查询.给出了建立模糊数据库的实现过程,对模糊数据库进行查询并给出实验结果.结果证明由关系型数据库转为模糊数据库后性能相近,可行性高.     

模糊强化学习在机器人导航中的应用

提出了一种基于自适应模糊RBF神经网络的Q学习算法。在模糊神经网络中引入Q学习,赋予模糊神经系统自学习和自适应能力。详细研究了模糊RBF神经网络的五层结构以及Q学习算法,给出了算法设计的流程图,所提算法对移动机器人在未知环境的自主导航,在未知环境中的自学习和自适应具有重要的应用价值。     

Application of fuzzy logic to reliability engineering

The analysis of system reliability often requires the use of subjective-judgments, uncertain data, and approximate system models. By allowing imprecision and approximate analysis fuzzy logic provides an effective tool for characterizing system reliability in these circumstances; it does not force precision where it is not possible. Here we apply the main concepts of fuzzy logic, fuzzy arithmetic and linguistic variables to the analysis of system structures, fault trees, event trees, the reliability of degradable systems, and the assessment of system criticality based on the severity of a failure and its probability of occurrence     

模糊算法在态势评估中的应用

针对态势评估中存在的不确定性,把模糊算法应用于态势评估,建立了基于模糊算法的态势评估框架,解析态势评估的流程。同时根据不同的评估需要,建立两种不同的态势评估模型,并给出确立评估矩阵的隶属函数。通过实例把态势评估元素的条件隶属度进行量化,从而得出具体的评估矩阵,对基于模糊算法的态势评估方法的整个实现过程进行讨论。最后,通过分析评估结果比较两种模型的效能,得出更适于态势评估的模型。     

模糊数据挖掘在客户关系管理中的应用

应用模糊数据挖掘方法，给出了模糊数据挖掘中基于聚类分析的算法以及详细的模糊聚类分析步骤，对复杂的客户需求进行合理聚类，充分体现客户需求的个性化与产品模块的完备性。模糊数据挖掘能够对将来的趋势和行为进行预测，从而很好地支持人们的决策。