基于RBF神经网络的人类活动识别

为了实现对智能家居中人类日常生活活动（ADLS）的识别,将使用径向基函数RBF神经网络来进行人类活动的识别.并使用志愿者在智能家居试验台执行活动搜集到的数据对算法的准确率进行评估.实验结果表明,选择合适的特征量和参数,相比于隐含马尔科夫模型径向基函数神经网络人类活动的识别方面显示了较高的准确率.     

Mixed-band wavelet-chaos-neural network methodology for epilepsy and epileptic seizure detection

A novel wavelet-chaos-neural network methodology is presented for classification of electroencephalograms (EEGs) into healthy, ictal, and interictal EEGs. Wavelet analysis is used to decompose the EEG into delta, theta, alpha, beta, and gamma sub-bands. Three parameters are employed for EEG representation: standard deviation (quantifying the signal variance), correlation dimension, and largest Lyapunov exponent (quantifying the non-linear chaotic dynamics of the signal). The classification accuracies of the following techniques are compared: (1) unsupervised k-means clustering; (2) linear and quadratic discriminant analysis; (3) radial basis function neural network; (4) Levenberg-Marquardt backpropagation neural network (LMBPNN). To reduce the computing time and output analysis, the research was performed in two phases: band-specific analysis and mixed-band analysis. In phase two, over 500 different combinations of mixed-band feature spaces consisting of promising parameters from phase one of the research were investigated. It is concluded that all three key components of the wavelet-chaos-neural network methodology are important for improving the EEG classification accuracy. Judicious combinations of parameters and classifiers are needed to accurately discriminate between the three types of EEGs. It was discovered that a particular mixed-band feature space consisting of nine parameters and LMBPNN result in the highest classification accuracy, a high value of 96.7%.     

Minimal radial basis function neural networks for nonlinear channelequalisation

The results of linear and nonlinear channel equalisation in data communications are presented, using a previously developed minimal radial basis function neural network structure, referred to as the minimal resource allocation network (MRAN). The MRAN algorithm uses online learning, and has the capability to grow and prune the RBF network's hidden neurons ensuring a parsimonious network structure. Compared to earlier methods, the proposed scheme does not have to estimate the channel order first, and fix the model parameters. Results showing the superior performance of the MRAN algorithm for two linear channels (minimum and non-minimum phase) for 2PAM signalling, and three nonlinear channels for 2PAM and 4QAM signalling, are presented     

基于径向基函数神经网络的模拟/混合电路故障诊断

径向基函数神经网络是一种前馈型神经网络,具有较强的函数逼近能力和分类能力,学习速度快等优点.本文采用幅值恒定的正弦信号源进行模拟电路的故障仿真,从频域提取输出信号波形的特征值建立故障字典,应用径向基函数神经网络的这些优点进行响应分析和故障诊断,能够实现快速故障诊断及定位,具有准确率高的特点.     

Selection of weight quantisation accuracy for radial basis function neural network using stochastic sensitivity measure

Minimising the number of bits per connection weight in hardware realisation of a radial basis function neural network (RBFNN) will result in high-speed and low-cost implementation, with possible increase in output error. A weight quantisation accuracy selection method is proposed, to find an appropriate number of bits for a given stochastic sensitivity measure, which quantifies the relationship between the variance of the output error and first- and second-order statistics of input, weight and their perturbations.     

Flood forecasting using radial basis function neural networks

A radial basis function (RBF) neural network (NN) is proposed to develop a rainfall-runoff model for three-hour-ahead flood forecasting. For faster training speed, the RBF NN employs a hybrid two-stage learning scheme. During the first stage, unsupervised learning, fuzzy min-max clustering is introduced to determine the characteristics of the nonlinear RBFs. In the second stage, supervised learning, multivariate linear regression is used to determine the weights between the hidden and output layers. The rainfall-runoff relation can be considered as a linear combination of some nonlinear RBFs. Rainfall and runoff events of the Lanyoung River collected during typhoons are used to train, validate,and test the network. The results show that the RBF NN can be considered a suitable technique for predicting flood flow     

球磨机对象控制中模糊径向基函数神经网络的PID控制分析

结合球磨机制粉系统的特点,提出球磨机对象控制中模糊径向基函数神经网络PID控制算法,结合混合优化算法,在混沌粒子群优化的同时实现粗线调,并应用BP算法做好在线细调,进而得到PID控制的最佳参数。通过Matla对算法进行仿真,结果表明,系统不仅有效解决了球磨机复杂对象的控制问题,同时也实现了算法的快速收敛,并有较快的跟踪速度以及较小的超调,解耦较好,适应性较强。     

Interpolation capability of the periodic radial basis function network

A periodic radial basis function (RBF) network based on the regularisation approach is proposed. The periodic RBF network can eliminate the Gibbs phenomenon observed in the conventional RBF network at the boundary of the data. For the evaluation of the interpolation capability, the frequency response of the periodic RBF network is analysed. It is then theoretically shown that the frequency response is asymptotically equivalent to the ideal sinc interpolation, and that the RBF interpolation is closer to the ideal sinc interpolation than the cubic spline and Lanczos interpolations     

粒子群RBF神经网络电力电子电路故障诊断

为检测和诊断电力电子电路中的故障,获得更高的诊断精确度,提出粒子群算法优化RBF神经网络的故障诊断方法.与基本RBF神经网络相比,粒子群RBF神经网络可以提高系统的收敛速度和精度.把通过特征提取获得的电力电子电路故障特征量作为神经网络的输入,利用训练好的粒子群优化后的RBF神经网络进行故障诊断.仿真结果表明,实际输出与期望输出基本吻合,具有良好的分类效果,能够提高诊断精确度,对于电力电子电路的故障诊断是一种有效的方法.     

Improved vocal effort modeling by exploiting echo state network and radial basis function network

The independent hypothesis between frames in vocal effect(VE) recognition makes it difficult for frame based spectral features to describe the intrinsic temporal correlation and dynamic change information in speech phenomena. A novel VE detection method based on echo state network(ESN) is proposed. The input sequences are mapped into a fixed-dimensionality vector in high dimensional coding space by reservoir of the ESN. Then, radial basis function(RBF) networks are employed to fit the probability density function(pdf) of each VE mode by using the vectors in the high dimensional coding space. Finally, the minimum error rate Bayesian decision is employed to judge the VE mode. The experiments which are conducted on isolated words test set achieve 79.5% average recognition accuracy, and the results show that the proposed method can overcome the defect of the independent hypothesis between frames effectively.     

基于改进的径向基函数神经网络的辐射源算法研究

多传感器数据融合系统中辐射源识别技术占有重要的位置。本文结合改进的径向基网络给出了辐射源算法的实现结构。结合辐射源预分的数据特点对径向基中高斯核函数进行了修改,使得在不对处理样本初始化的条件下仍有很好的预分效果,预分后采用模糊匹配的方法,完成辐射源的识别。     

A Beta basis function neural network in CMOS subthreshold mode

Beta basis function neural networks (BBFNNs) are powerful systems for learning and universal approximation. In this paper, we present a hardware implementation of the Beta neuron using the CMOS subthreshold mode. We describe the low power–low voltage analogue Beta neuron circuit. Three main modules are used to realize the electronic Beta function: a logarithmic currentto-voltage converter, a multiplier and an exponential voltage-to-current converter. Simulation results show the validity of our neural hardware implementation. The parameters of the electronic Beta function are controlled independently by current sources. This analogue implementation could be used easily to realize analogue BBFNNs.     

A dynamic regularized radial basis function network for nonlinear,nonstationary time series prediction

In this paper, constructive approximation theorems are given which show that under certain conditions, the standard Nadaraya-Watson (1964) regression estimate (NWRE) can be considered a specially regularized form of radial basis function networks (RBFNs). From this and another related result, we deduce that regularized RBFNs are m.s., consistent, like the NWRE for the one-step-ahead prediction of Markovian nonstationary, nonlinear autoregressive time series generated by an i.i.d. noise processes. Additionally, choosing the regularization parameter to be asymptotically optimal gives regularized RBFNs the advantage of asymptotically realizing minimum m.s. prediction error. Two update algorithms (one with augmented networks/infinite memory and the other with fixed-size networks/finite memory) are then proposed to deal with nonstationarity induced by time-varying regression functions. For the latter algorithm, tests on several phonetically balanced male and female speech samples show an average 2.2-dB improvement in the predicted signal/noise (error) ratio over corresponding adaptive linear predictors using the exponentially-weighted RLS algorithm. Further RLS filtering of the predictions from an ensemble of three such RBFNs combined with the usual autoregressive inputs increases the improvement to 4.2 dB, on average, over the linear predictors     

Real-time data-reusing adaptive learning of a radial basis function network for tracking evoked potentials

Tracking variations in both the latency and amplitude of evoked potential (EP) is important in quantifying properties of the nervous system. Adaptive filtering is a powerful tool for tracking such variations. In this paper, a data-reusing non-linear adaptive filtering method, based on a radial basis function network (RBFN), is implemented to estimate EP. The RBFN consists of an input layer of source nodes, a single hidden layer of non-linear processing units and an output layer of linear weights. It has built-in nonlinear activation functions that allow learning of function mappings. Moreover, it produces satisfactory estimates of signals against a background noise without a priori knowledge of the signal, provided that the signal and noise are independent. In clinical situations where EP responses change rapidly, the convergence rate of the algorithm becomes a critical factor. A carefully designed data-reusing RBFN can accelerate the convergence rate markedly and, thus, enhance its performance. Both theoretical analysis and simulation results support the improved performance of our new algorithm.     

A robust collision prediction and detection method based on neural network for autonomous delivery robots

For safe last-mile autonomous robot delivery services in complex environments, rapid and accurate collision prediction and detection is vital. This study proposes a suitable neural network model that relies on multiple navigation sensors. A light detection and ranging technique is used to measure the relative distances to potential collision obstacles along the robot's path of motion, and an accelerometer is used to detect impacts. The proposed method tightly couples relative distance and acceleration time-series data in a complementary fashion to minimize errors. A long short-term memory, fully connected layer, and SoftMax function are integrated to train and classify the rapidly changing collision countermeasure state during robot motion. Simulation results show that the proposed method effectively performs collision prediction and detection for various obstacles.     

Optical flow estimation and moving object segmentation based onmedian radial basis function network

Various approaches have been proposed for simultaneous optical flow estimation and segmentation in image sequences. In this study, the moving scene is decomposed into different regions with respect to their motion, by means of a pattern recognition scheme. The inputs of the proposed scheme are the feature vectors representing still image and motion information. Each class corresponds to a moving object. The classifier employed is the median radial basis function (MRBF) neural network. An error criterion function derived from the probability estimation theory and expressed as a function of the moving scene model is used as the cost function. Each basis function is activated by a certain image region. Marginal median and median of the absolute deviations from the median (MAD) estimators are employed for estimating the basis function parameters. The image regions associated with the basis functions are merged by the output units in order to identify moving objects.     

Improving training of radial basis function network forclassification of power quality disturbances

Features extracted from non-stationary and transitory power quality disturbances using wavelet transform modulus maxima can serve as powerful discriminating features for wavelet-based classification of these disturbances. Using these features, a comprehensive 'knowledge-based' algorithm is proposed for the training of the radial basis function network classifier, so that at its convergence the network gives both the optimal feature weight vector as well as the cluster centres and scaling widths     

基于免疫机制的RBF网络在OFDM信道估计中的应用

针对OFDM系统的信道估计问题，提出了一种新型的基于免疫机制的RBF网络信道估计方法。该算法能够有效提高网络训练精度，改善进化训练算法的未成熟收敛问题。在低信噪比，快衰落的恶劣信道情况下，文中采用的RBF网络相对传统算法表现出了优异性的性能，设计出的信道估计器可以达到很高的测量精度。     

An improved radial basis function based method for image warping

A new image warping method is proposed in this letter, which can warp a given image by some manual defined features. Based on the radial basis interpolation function algorithm, the proposed method can transform the original optimized problem into nonsingular linear problem by adding one-order term and affine differentiable condition. This linear system can get the steady unique solution by choosing suitable kernel function. Furthermore, the proposed method demonstrates how to set up the radial basis function in the target image so as to achieve supports to adopt the backward re-sampling technology accordingly which could gain the very slippery warping image. The experimental result shows that the proposed method can implement smooth and gradual image warping with multi-anchor points‘ accurate interpolation.