Zheng back to China with future uncertain

中国足球队长郑智结束在英格兰Charlton Athletic的租借期后返回母队Shandong Luneng。他在12场比赛中表现出色,尽管未能帮助球队避免从英超降级。未来何去何从,郑智表示将由Shandong Luneng决定。
BEIJING, May 14 - China captain Zheng Zhi, who is preparing to head home after his loan spell in England with Charlton Athletic, says his long-term future is in the hands of his club Shandong Luneng.

Zheng was loaned to Charlton by the Chinese champions in January and impressed in 12 appearances during what ultimately proved to be a fruitless battle against relegation from the Premier League.

Charlton had an option on a permanent deal for the 26-year-old, who missed a hat-trick of chances in Sunday's 2-2 draw at Liverpool, but Zheng was quoted last month as saying he would leave the Valley if the Londoners went down.

"Frankly speaking I have no idea about my future," the versatile midfielder told Monday's Titan Sport.

"What I can do at the moment is collect all the things here and go back to Shandong Luneng... All I can say is everything in England has come to a full stop.

"I was loaned by Luneng. What I do and where I go next should be decided by the club."

Zheng, who was voted Charlton's player of the month in March, said whatever happened he would never forget his time in South London.

"In my opinion, the Premier League is the toughest league in the world," he added.

"I will miss the days in Charlton. This experience was the most unforgettable of my life. I still have my dreams and the ability to carry out these dreams, so I have no regrets."

The Chinese Super League (CSL) season is only 10 matches old but Zheng is likely to restrict his appearances for Shandong to the East Asian Champions (A3) Cup from June 7-13 before China's Asian Cup finals campaign starts in July.


read an enlish article everyday,read more and get more.

MATLAB主动噪声和振动控制算法——对较大的次级路径变化具有鲁棒性内容概要:本文主要介绍了一种在MATLAB环境下实现的主动噪声和振动控制算法,该算法针对较大的次级路径变化具有较强的鲁棒性。文中详细阐述了算法的设计原理与实现方法,重点解决了传统控制系统中因次级路径动态变化导致性能下降的问题。通过引入自适应机制和鲁棒控制策略,提升了系统在复杂环境下的稳定性和控制精度,适用于需要高精度噪声与振动抑制的实际工程场景。此外,文档还列举了多个MATLAB仿真实例及相关科研技术服务内容,涵盖信号处理、智能优化、机器学习等多个交叉领域。; 适合人群:具备一定MATLAB编程基础和控制系统理论知识的科研人员及工程技术人员,尤其适合从事噪声与振动控制、信号处理、自动化等相关领域的研究生和工程师。; 使用场景及目标:①应用于汽车、航空航天、精密仪器等对噪声和振动敏感的工业领域;②用于提升现有主动控制系统对参数变化的适应能力;③为相关科研项目提供算法验证与仿真平台支持; 阅读建议:建议读者结合提供的MATLAB代码进行仿真实验,深入理解算法在不同次级路径条件下的响应特性,并可通过调整控制参数进一步探究其鲁棒性边界。同时可参考文档中列出的相关技术案例拓展应用场景。
### 关于机器学习中的不确定权重 在机器学习和算法领域,“不确定权重”通常指的是模型参数或特征的重要性无法被精确量化的情况。这种不确定性可能来源于数据分布的变化、噪声干扰或是模型自身的局限性。 #### 不确定性的来源 当讨论到“不确定权重”,可以从以下几个方面来理解其背景: 1. **概念漂移 (Concept Drift)** 在处理实时数据流时,建立框架以评估能够应对概念漂移的学习算法是非常重要的[^1]。如果数据随时间变化,则用于训练模型的权重可能会变得不再适用。因此,在动态环境中调整权重成为关键挑战之一。 2. **数学建模下的权衡** 假设存在一种数学模型描述了某台机器的能力指标:T(m)表示思考能力;C(m),计算能力;U(m),理解力;S(m),自我意识水平[^2]。可以推测这些因素之间可能存在某种关联关系影响最终决策过程中的加权方式。然而实际应用中往往难以确切定义每个维度的具体贡献度,这就引入了所谓的“不确定”。 3. **贝叶斯方法的应用** 贝叶斯理论提供了一种有效途径去解决这类问题——通过概率分布形式表达变量间的依赖结构并更新先验信念至后验估计状态。例如对于线性回归任务而言,我们可以设定目标函数如下所示: ```python import numpy as np def bayesian_linear_regression(X, y, alpha=1., beta=100.): """ Perform Bayesian Linear Regression. Parameters: X : array-like of shape (n_samples, n_features) Training data features. y : array-like of shape (n_samples,) Target values. alpha : float, default=1. Hyperparameter controlling variance of weights prior distribution. beta : float, default=100. Noise precision parameter. Returns: w_mean : ndarray of shape (n_features,) Posterior mean estimate for the regression coefficients. cov_matrix : ndarray of shape (n_features, n_features) Covariance matrix representing uncertainty around `w`. """ Sigma_N_inv = alpha * np.eye(X.shape[1]) + beta * X.T @ X mu_N = beta * np.linalg.inv(Sigma_N_inv) @ X.T @ y return mu_N, np.linalg.inv(Sigma_N_inv) ``` 上述代码片段展示了如何利用贝叶斯视角下完成简单版线性回归分析的同时也保留住了关于系数向量潜在波动的信息(即协方差矩阵)。这正是我们所提到的那种“模糊边界”的体现形式之一。 #### 总结说明 综上所述,“不确定权重”这一术语广泛存在于各类复杂场景之中,无论是面对不断演变的数据环境还是试图捕捉抽象概念间微妙联系的时候都会遇到它身影。而借助统计学工具比如贝叶斯推断可以帮助缓解部分难题带来的困扰。
评论
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值