用python，采用径向基神经网络模型将鸢尾花数据二分类。

好的，针对您的问题，我可以给出以下步骤：

1. 首先，导入必要的库：

import numpy as np
import pandas as pd
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import StandardScaler
from sklearn.metrics.pairwise import rbf_kernel

2. 加载鸢尾花数据集并进行预处理：

iris = datasets.load_iris()

# 取前两类鸢尾花数据作为样本
X = iris.data[:100, :]
y = iris.target[:100]

# 数据标准化
scaler = StandardScaler()
X = scaler.fit_transform(X)

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

3. 定义径向基函数：

def rbf(x, c, s):
    return np.exp(-1 / (2 * s**2) * (x-c)**2)

4. 训练径向基神经网络模型：

class RBFNet:
    def __init__(self, hidden_size, std):
        self.hidden_size = hidden_size
        self.std = std
        self.centers = None
        self.weights = None

    def train(self, X, y):
        # 随机从训练数据中选取中心点
        rnd_idx = np.random.permutation(len(X))[:self.hidden_size]
        self.centers = X[rnd_idx]

        # 计算径向基函数值
        G = rbf_kernel(X, self.centers, gamma=1.0/self.std)

        # 计算权重
        self.weights = np.dot(np.linalg.pinv(G), y)

    def predict(self, X):
        G = rbf_kernel(X, self.centers, gamma=1.0/self.std)
        y_pred = np.dot(G, self.weights)
        return np.round(y_pred)

5. 测试模型性能：

rbfnet = RBFNet(hidden_size=10, std=1.0)
rbfnet.train(X_train, y_train)
y_pred = rbfnet.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print('Accuracy:', accuracy)

以上就是用 Python 和径向基神经网络模型将鸢尾花数据二分类的步骤，希望能对您有所帮助！