我是一个机器学习的新手，在转向多层网络之前，我一直在尝试使用基本的感知器进行实验。

我的问题出在下面的代码中。我有一个训练数据生成器，它使用一组权重来生成真值表。

我的问题是，当训练数据使用’A’组权重生成时，感知器能够解决/确定这组权重，但使用’B’组权重生成时却不能。当给定使用’B’组权重生成的训练数据时，它会持续陷入无限循环，试图确定权重（这是局部最小值问题吗？）

我不完全明白为什么会发生这种情况。任何帮助或建议都将不胜感激。

提前感谢。

// 调用函数 public static void TestPerceptron ()    {        // 问题:         // 当使用'A'组权重生成训练数据时，感知器能够根据给定的训练数据确定正确的权重。        // 当使用'B'组权重生成训练数据时，感知器永远无法完成训练，并陷入无限循环        double[] weights = new double[] {            //3,2,2,3 // A            3,2,1,3,1 // B        };        double bias = 0.0;         var trainingData = PerceptronHelper.GenerateDataSetUsingWeights (weights, bias);        var perceptron = new Perceptron ();        perceptron.Train (trainingData, null, null);        //perceptron.Train (trainingData, weights, bias);     }public class Perceptron{    private static Random r = new Random ();    protected double _bias = r.NextDouble();    protected double[] _weights;    protected virtual double ComputeOutput(double[] weights, double[] inputs, double bias)    {        var total = 0.0;        for (var index = 0; index < inputs.Length-1; index++)         {            total += weights [index] * inputs [index];        }        return total + (1 * bias);    }    protected virtual void SetWeights(ref double[] weights, double[] inputs, double error, double learningRate, ref double bias)    {        for (var index = 0; index < inputs.Length-1; index++)         {            weights[index] = weights [index] + (learningRate * error * inputs [index]);        }        bias += learningRate * error * 1;    }    public virtual void Train(double[][] trainingData, double[] idealWeights, double? idealBias)    {        var learningRate = 1.0;        var totalError = 1.0;        var targetError = 0.0;        var epochs = 0.0;        var bias = _bias;        var weights = new double[trainingData[0].Length-1];        if (idealBias.HasValue)            bias = idealBias.Value;        if (idealWeights != null)            weights = idealWeights;        while (totalError > targetError)         {            totalError = 0.0;            for (var index = 0; index < trainingData.Length; index++)             {                var inputs = trainingData [index];                // 获取目标                var target = inputs [inputs.Length - 1];                // 计算输出                var computed = ComputeOutput (weights, inputs, bias);                // 通过激活函数传递计算结果                var output = PerceptronHelper.Activation (computed);                // 确定误差                 var error = (target - output);                // 调整权重                SetWeights (ref weights, inputs, error, learningRate, ref bias);                totalError += Math.Abs(error);                var weightsMsg = "Weights: ";                foreach(var weight in weights)                {                    weightsMsg += weight + "|";                }                Console.WriteLine (String.Format ("error: {0} weights: {1} bias: {2}", totalError, weightsMsg, bias));            }            epochs++;        }        _bias = bias;        _weights = weights;    }    public void Predict(double[] inputs)    {        var sum = 0.0;        for (var index = 0; index < inputs.Length; index++)         {            sum += inputs [index] * _weights [index] + 1 * _bias;            Console.WriteLine (String.Format("input: {0} weight: {1} bias: {2}", inputs[index], _weights[index], _bias));        }        var output = PerceptronHelper.Activation (sum);        Console.WriteLine ("Output:{0}", output);    }}public static class PerceptronHelper{    // 根据给定的权重生成训练数据 - 输入的数量 = 权重的数量     public static double[][] GenerateDataSetUsingWeights(double[] idealWeights, double bias)    {        var weights = idealWeights;        var inputs = new double[weights.Length];        var numInputCombinations = Math.Pow(2,inputs.Length);         var trainData = new double[(int)numInputCombinations][];        int inputValue = 0;        // 生成训练数据        for (var index = 0; index < numInputCombinations; index++)         {            var sum = 0.0;              // 数组的最后一个项目是预期输出            var trainDataLine = new double[weights.Length+1];            var binary = Convert.ToString (inputValue, 2);            binary = binary.PadLeft (weights.Length, '0');            // 创建训练数据行            for (var wIndex = 0; wIndex < weights.Length; wIndex++)             {                inputs [wIndex] = double.Parse(binary[wIndex].ToString());                trainDataLine [wIndex] = inputs [wIndex];                sum += inputs [wIndex] * weights [wIndex];            }            sum += (1 * bias);            var output = Activation (sum);            // 将预期结果存储在数组的最后一个项目中            trainDataLine [weights.Length] = output;            // 将行添加到数据中            trainData[index] = trainDataLine;            inputValue++;        }        return trainData;    }    public static double Activation (double sum)     {        Console.WriteLine (String.Format("evaluating :{0}", sum));        return Math.Abs(sum) >= 5 ? 1 : 0;    }}

输出示例：

https://onedrive.live.com/redir?resid=8a0ad995f81066db!176&authkey=!AGgY8Iy4g_8lpv4&ithint=file%2crtf

回答：

将学习率改为0.075解决了这个问题。