According to the relationship between organic pollutants concentration in river water environment and the influential factors , a rbf model has been used to applied to prediction of organic pollutant concentration of water environment based on the field observation data 根据河流有机污染物的浓度与其影响因素之间存在的映射关系，以实际监测数据为基础，建立了一个水环境有机污染物浓度预测的径向基函数网络模型。
3 ) in the classification networks , this paper not only introduces the traditional center clustering method and the back - propagation ( bp ) network , but also applies a hidden - layer - structure - adaptive radial basis function ( rbf ) network to fingerprint classification 3 )在指纹分类的算法上，本论文不仅探讨了比较传统的中心聚类方法、 bp网聚类方法，还将一种自适应调整隐节点个数的径向基函数网络运用到指纹分类的问题上。
This method tackles the first problem excellent because it considers the discrimination between different clusters in a class , as for the second problem of multi - classes , we can settle it easily too : if the output of more than one rbf networks exceed the threshold value , then we can say that the new text vector belongs to these accordingly classes simultaneousl y 由于本方法考虑到了每个类内部不同簇之间的差异性，因此很好的解决了前述的第一个问题。这种方法也可以解决上述的“兼类”问题，若新文本的特征向量在多个类的径向基函数网络上的输出值都超过了阈值，则可认为它同时属于这几个类。
Third , based on the non - linearity feature of the system , the lmf algorithm is given by using the non - linearity least square optimal method . the simulating results show that the accuracy of the algorithm approaches the cramer - rao - bound . finally , the radial based function network localization algorithm is present for bistatic sonar 为了克服lmf算法在进行迭代时依赖于初始值的缺点，根据人工神经网络在求解非线性问题方面的特点，提出了用径向基函数网络方法来进行双基地声呐定位的优化算法。
The difference between bp networks and radial basis function networks is pointed out , and we discuss the decision of two important parameters of radial basis function networks . also , we improved on hcm algorithm which is often used to forecast by combining input vector with output vector and getting extended vector when we decide key parameter of radial basis function networks - - - - center vector 文中指出了bp网络与径向基函数网络的区别，讨论了径向基函数网络的两个重要参数的设定。在确定径向基函数网络的关键参数? ?中心向量时，通过结合输入输出向量得到扩展向量的方式改进了径向基函数网络用于预测中常用的hcm算法。
In conclusion , based on radial basis function neural networks , a method of identifying vehicle steering angle is proposed for further investigation of the vehicle handling inverse dynamics . the identification results show that the method is not only feasible , but also with high accuracy , little computation requirement and good stability 综上所述,本文利用径向基函数网络建立了汽车横摆角速度响应与方向盘转角的非线性映射关系,根据仿真试验测得的数据,由汽车的横摆角速度响应识别出了方向盘的转角输入,具有很高的精度和抗干扰能力。
In this dissertation , base on the review and analysis of current mainstream algorithms and techniques , we build up whole rtr system , and study some efficient recognition methods for different radar characters . the major research work and contributions in this dissertation are summarized as below : 1 . summarized current popular pattern recognition methods , this dissertation researched in the algorithms and performances of nearest neighbor ( nn ) classifier , multi - layer perceptron and rbfn ( radial basis function network ) 本文在总结当前主流雷达目标识别算法的基础上,建立起基于gbr的雷达目标识别系统,并且对弹道导弹的各种特识别方法进行了研究,主要进行的工作和创新有: 1 .研究和总结了当前常用的分类识别方法,针对雷达目标识别的特点,对近邻分类器、多层感知器和径向基函数网络( rbfn )分类器的算法和性能进行了研究。
The equations derived are more complicated if more precise model is employed for high accuracy . the technique , which does n ' t need to have an explicit model - calibration based on neural networks implicit vision model , is more effective . since bp neural network can implement any nonlinear relationship from input to output and need n ' t to model , and the classical stereo vision approach based on explicit model are very complicated , an algorithm of stereo vision based on bp neural networks implicit vision model is proposed 利用神经网络可以充分逼近任意的非线性关系且无须精确建模的特点，针对传统的立体视觉方法过程繁琐，对安装精度要求高的不足，本文提出了一种基于bp神经网络隐式视觉模型的立体视觉方法，该算法实施起来比较简便；针对已有的像差修正算法计算过程复杂的不足，提出了一种基于bp神经网络的修正成像误差的算法；针对具有共面特征的点的三维重构的应用，提出了一种基于径向基函数网络的二维平面测量算法。
Through study of correlative contents of advanced computer cybernetics , artificial intelligence , the domain knowledge and special crop growth mechanism in greenhouse , we present the system of multi - sensor data fusion ( msdf ) based on radial basis function network ( rbf ) to implement on line detection for nutrient - liquid , which may realize multiple components detection on - line , for example no3 - , cl - , ca2 + , ph , ec , nh4 + , k + and so on . the soft sensor ' s mechanism is introduced to overcome the limitations of sensor ' s manufacturing process . to improve the believe - degree of soft sensor ' s result , we analyze soft sensor ' s result by uncertain inferential capacity and combination rule of evidential theory 本论文通过对计算机技术、控制理论、人工智能技术和设施农业领域知识等相关理论的研究，结合对特定温室蔬菜生长的研究与机理分析，提出了一种基于rbf神经网络的营养液多传感器数据融合( msdf )系统，实现对营养液组分： no _ 3 ~ - 、 cl ~ - 、 ca ~ ( 2 + ) 、 ph 、 ec 、 nh _ 4 ~ +和k ~ +的在线检测；对于由于目前传感器制造工艺的限制而不能在线检测的离子成分如磷酸根和硫酸根，提出了一种基于径向基函数网络的软测量机制，可以有效地实现对营养液中磷酸根和硫酸根成分的实时检测；为了提高软测量结果的可信度，利用d - s证据理论的不确定推理能力和合成公式，结合领域知识对软测量结果进行可信度分析。
