我在为Google科学博览会创建一个AI系统时遇到了一个难题，无法找到标题中提到的这个问题的错误。在Google上搜索后，该主题没有返回任何答案。我使用的是Monodevelop；

这是我的代码：

    using UnityEngine;using System;using System.Collections;using System.Collections.Generic;using System.ComponentModel;using System.ComponentModel.Design;namespace NeuralNetwork {public class DecisionMaking {    public static string DecideString(List<string> choice){                    choice.ToArray ();                    int mychoice = UnityEngine.Random.Range (0, choice.Count);                    return choice [mychoice];            }    public static Vector3 DecideVector(List<Vector3> choice){        choice.ToArray ();        int mychoice = UnityEngine.Random.Range (0, choice.Count);        return choice [mychoice];    }}public class Network {    private int num_in;    private int num_hid;    private int num_out;    private double[,] i_to_h_wts;    private double[,] h_to_o_wts;    private double[] inputs;    private double[] hidden;    private double[] outputs;    private double learningRate = 0.3;    private System.Random gen = new System.Random();    public static bool isTraining;    public delegate void ChangeHandler (System.Object sender, EventArgs nne);    public event ChangeHandler Change;    #region Constructor    public Network (int num_in,int num_hid, int num_out){        this.num_in = num_in;        this.num_hid = num_hid;        this.num_out = num_out;        i_to_h_wts = new double[num_in + 1, num_hid];        h_to_o_wts = new double[num_hid + 1, num_out];        inputs = new double[num_in + 1];        hidden = new double[num_hid + 1];        outputs = new double[num_out];    }    #endregion    public void initializeNetwork() {                    inputs [num_in] = 1.0;                    inputs [num_hid] = 1.0;                    for (int i = 0; i < num_in + 1; i++) {                            for (int j = 0; j < num_hid; j++) {                                    i_to_h_wts [i, j] = (gen.NextDouble () * 4) - 2;                            }                    }                    for (int i = 0; i < num_hid +1; i++) {                            for (int j = 0; j < num_out; j++) {                                    h_to_o_wts [i, j] = (gen.NextDouble () * 4) - 2;                            }                    }            }    public virtual void On_Change(NeuralNetworkEventArgs nne) {                    if (Change != null) {                            Change (this, nne);                    }            }    public void pass_forward(double[] applied_inputs, double[] targOuts) {                    for (int i = 0; i < num_in; i++) {                            inputs [i] = applied_inputs [i];                    }                    for (int i = 0; i < num_hid; i++) {                            double sum = 0.0;                            for (int j = 0; j < num_in + 1; j++) {                                    sum += inputs [j] * i_to_h_wts [j, i];                            }                            hidden [i] = SigmoidActivationFunction.processValue (sum);                    }                    for (int i = 0; i < num_out; i++) {                            double sum = 0.0;                            for (int j = 0; j < num_hid + 1; j++) {                                    sum += hidden [j] * h_to_o_wts [j, i];                            }                            outputs [i] = SigmoidActivationFunction.processValue (sum);                            NeuralNetworkEventArgs nne = new NeuralNetworkEventArgs (outputs, targOuts);                            On_Change (nne);                    }            }    #region Public Properties / Methods    /// <summary>    /// gets / sets the number of input nodes for the Neural Network    /// </summary>    public int NumberOfInputs    {        get { return num_in; }        set { num_in = value; }    }    /// <summary>    /// gets / sets the number of hidden nodes for the Neural Network    /// </summary>    public int NumberOfHidden    {        get { return num_hid; }        set { num_hid = value; }    }    /// <summary>    /// gets / sets the number of output nodes for the Neural Network    /// </summary>    public int NumberOfOutputs    {        get { return num_out; }        set { num_out = value; }    }    /// <summary>    /// gets / sets the input to hidden weights for the Neural Network    /// </summary>    public double[,] InputToHiddenWeights    {        get { return i_to_h_wts; }        set { i_to_h_wts = value; }    }    /// <summary>    /// gets / sets the hidden to output weights for the Neural Network    /// </summary>    public double[,] HiddenToOutputWeights    {        get { return h_to_o_wts; }        set { h_to_o_wts = value; }    }    /// <summary>    /// gets / sets the input values for the Neural Network    /// </summary>    public double[] Inputs    {        get { return inputs; }        set { inputs = value; }    }    /// <summary>    /// gets / sets the hidden values for the Neural Network    /// </summary>    public double[] Hidden    {        get { return hidden; }        set { hidden = value; }    }    /// <summary>    /// gets / sets the outputs values for the Neural Network    /// </summary>    public double[] Outputs    {        get { return outputs; }        set { outputs = value; }    }    /// <summary>    /// gets / sets the LearningRate (eta) value for the Neural Network    /// </summary>    public double LearningRate    {        get { return learningRate; }        set { learningRate = value; }    }    /// <summary>    /// Gets the error for this NeuralNetwork, based on targets - outputs    /// </summary>    /// <param name="targets">The target values, can be {0.0,1.0} or {1.0,0.0}</param>    /// <returns>the total error for this ANN based on targets - outputs</returns>    public double getError(double[] targets)    {        //storage for error        double error = 0.0;        //this calculation is based on something I read about weight space in        //Artificial Intellegence - A Modern Approach, 2nd edition.Prentice Hall        //2003. Stuart Rusell, Peter Norvig. Pg 741        error = Math.Sqrt(Math.Pow((targets[0] - outputs[0]), 2));        return error;    }    #endregion}public class NeuralNetworkEventArgs : EventArgs {    private double[] targOuts;    private double[] outputs;    public NeuralNetworkEventArgs(double[] outputs,double[] targOuts){        this.targOuts = targOuts;        this.outputs = outputs;    }    public double[] TargetOuts {        get { return targOuts; }    }    public double[] Outputs {        get { return outputs;}    }}public class SigmoidActivationFunction {    public static double processValue(double x) {        return 1.0 / (1.0 + Math.Pow (Math.E, -x));    }}}

以及：

using System;using System.Collections.Generic;using System.Text;using NeuralNetwork;namespace GA_ANN_XOR{    #region NN_Trainer_XOR CLASS    /// <summary>    /// Provides a GA trainer for a    /// <see cref="NeuralNetwork">NeuralNetwork</see> class    /// with 2 inputs, 2 hidden, and 1 output, which is trying    /// to approximate the XOR problem    /// </summary>    public class GA_Trainer_XOR    {        #region Instance fields        private Random gen = new Random(5);        private int training_times = 10000;        private double[,] train_set =        {{0, 0},            {0, 1},            {1,0},            {1,1}};        //population size        private int POPULATION = 15;        //ANN's        private Network[] networks;        //Mutation        private double MUTATION = 0.5;        //Recombination        private double RECOMBINE = 0.4;        //flag to detect when we hav found good ANN        private bool foundGoodANN = false;        //number of outputs        private int trainLoop = 0;        //best configuration index        private int bestConfiguration = -1;        //acceptable overall Neural Networ error        private double acceptableNNError = 0.1;        //events for gui, generated by the GA trainer        public delegate void GAChangeHandler(Object sender, TrainerEventArgs te);        public event GAChangeHandler GAChange;        public event EventHandler GATrainingDone;        //events for gui, generated by the NeuralNetwork, but propgated up to gui        //by the GA trainer, thats why they this event is here, the gui knows nothing        //about the array of NeuralNetworks, so the event must come through trainer        public delegate void ChangeHandler(Object sender, NeuralNetworkEventArgs nne);        public event ChangeHandler NNChange;        #endregion        #region Public Properties/Methods        /// <summary>        /// Performs a microbial GA (best of last breeding cycle stays in population)        /// on an array of <see cref="NeuralNetwork"> NeuralNetworks</see> in an attempt        /// to find a solution to the XOR logix problem. The training presents the entire        /// training set to a random pair of <see cref="NeuralNetwork"> NeuralNetworks,</see>        ///  and evaluates which one does best. The winners genes, and some mutation are used        /// to shape the losers genes, in the hope that the new population will be moving        /// towards a closer solution.        /// </summary>        /// <param name="training_times">the number of times to carry out the        /// training loop</param>        /// <returns>The best <see cref="NeuralNetwork"> NeuralNetworks </see>        /// configuartion found</returns>        public Network doTraining(int training_times)        {            int a = 0;            int b = 0;            int WINNER = 0;            int LOSER = 0;            #region Training            //loop for the trainingPeriod            for (trainLoop = 0; trainLoop < training_times; trainLoop++)            {                //fire training loop event                TrainerEventArgs te = new TrainerEventArgs(trainLoop);                On_GAChange(te);                NeuralNetwork.isTraining = true;                //if the previous evaluation cyle, found a good ANN configuration                //quit the traning cycle, otherwise, let the breeding continue                if (foundGoodANN)                {                    break;                }                //pick 2 ANN's at random, GA - SELECTION                a = (int)(gen.NextDouble() * POPULATION);                b = (int)(gen.NextDouble() * POPULATION);                //work out which was the WINNER and LOSER, GA - EVALUATION                if (evaluate(a) < evaluate(b))                {                    WINNER = a;                    LOSER = b;                }                else                {                    WINNER = b;                    LOSER = a;                }                ////get the current value of the ANN weights                double[,] WINNER_i_to_h_wts = networks[WINNER].InputToHiddenWeights;                double[,] LOSER_i_to_h_wts = networks[LOSER].InputToHiddenWeights;                double[,] WINNER_h_to_o_wts = networks[WINNER].HiddenToOutputWeights;                double[,] LOSER_h_to_o_wts = networks[LOSER].HiddenToOutputWeights;                ////i_to_h_wts RECOMBINATION LOOP                for (int k = 0; k < networks[WINNER].NumberOfInputs + 1; k++)                {                    for (int l = 0; l < networks[WINNER].NumberOfHidden; l++)                    {                        //get genes from winner randomly for i_to_h_wts wieghts                        if (gen.NextDouble() < RECOMBINE)                        {                            // set the weights to be that of the input weights from GA                            LOSER_i_to_h_wts[k,l] = WINNER_i_to_h_wts[k,l];                        }                    }                }                //h_to_o_wts RECOMBINATION LOOP                for (int k = 0; k < networks[WINNER].NumberOfHidden + 1; k++)                {                    for (int l = 0; l < networks[WINNER].NumberOfOutputs; l++)                    {                        //get genes from winner randomly for i_to_h_wts wieghts                        if (gen.NextDouble() < RECOMBINE)                        {                            // set the weights to be that of the input weights from GA                            LOSER_h_to_o_wts[k,l] = WINNER_h_to_o_wts[k,l];                        }                    }                }                //i_to_h_wts MUTATION LOOP                for (int k = 0; k < networks[WINNER].NumberOfInputs + 1; k++)                {                    for (int l = 0; l < networks[WINNER].NumberOfHidden; l++)                    {                        //add some mutation randomly                        if (gen.NextDouble() < MUTATION)                        {                            LOSER_i_to_h_wts[k,l] += ((gen.NextDouble() * 0.2) - 0.1);                        }                    }                }                //h_to_o_wts MUTATION LOOP                for (int k = 0; k < networks[WINNER].NumberOfHidden + 1; k++)                {                    for (int l = 0; l < networks[WINNER].NumberOfOutputs; l++)                    {                        //add some mutation randomly                        if (gen.NextDouble() < MUTATION)                        {                            LOSER_h_to_o_wts[k,l] += ((gen.NextDouble() * 0.2) - 0.1);                        }                    }                }                //update the losers i_to_h_wts genotype                networks[LOSER].InputToHiddenWeights = LOSER_i_to_h_wts;                //update the losers i_to_h_wts genotype                networks[LOSER].HiddenToOutputWeights = LOSER_h_to_o_wts;            }            #endregion            //AT THIS POINT ITS EITHER THE END OF TRAINING OR WE HAVE            //FOUND AN ACCEPTABLE ANN, WHICH IS BELOW THE VALUE            //tell gui that training is now done            On_GATrainingDone(new EventArgs());            NeuralNetwork.isTraining = false;            //check to see if there was a best configuration found, may not have done            //enough training to find a good NeuralNetwork configuration, so will simply            //have to return the WINNER            if (bestConfiguration == -1)            {                bestConfiguration = WINNER;            }            //return the best Neural network            return networks[bestConfiguration];        }        /// <summary>        /// Is called after the initial training is completed.        /// Sipmly presents 1 complete set of the training set to        /// the trained network, which should hopefully get it pretty        /// correct now its trained        /// </summary>        public void doActualRun()        {            //loop through the entire training set            for (int i = 0; i <= train_set.GetUpperBound(0); i++)            {                //forward these new values through network                //forward weights through ANN                forwardWeights(bestConfiguration, getTrainSet(i));                double[] targetValues = getTargetValues(getTrainSet(i));            }        }        #endregion        #region Constructor        /// <summary>        /// Constructs a new GA_Trainer_XOR. The constructor creates        /// the population of <see cref="NeuralNetwork">NeuralNetworks</see>        ///  and also wires up the underlying <see cref="NeuralNetwork">        /// NeuralNetworks</see> events, to a new GA event, such that the        /// <see cref="NeuralNetwork">NeuralNetworks</see> event can be         /// propogated to the gui        /// </summary>        public GA_Trainer_XOR()        {            networks = new Network[POPULATION];            //create new ANN objects, random weights applied at start            for (int i = 0; i <= networks.GetUpperBound(0); i++)            {                networks[i] = new Network(2, 2, 1);                networks[i].Change += new Network.ChangeHandler(GA_Trainer_NN_Change);            }        }        #endregion        #region Events        /// <summary>        /// Raises the GA TrainingDone event        /// </summary>        /// <param name="te">The TrainerEventArgs</param>        public virtual void On_GATrainingDone(EventArgs ea)        {            if (GATrainingDone != null)            {                // Invokes the delegates.                 GATrainingDone(this, ea);            }        }        /// <summary>        /// Raises the GA Change event        /// </summary>        /// <param name="te">The TrainerEventArgs</param>        public virtual void On_GAChange(TrainerEventArgs te)        {            if (GAChange != null)            {                // Invokes the delegates.                 GAChange(this, te);            }        }        /// <summary>        /// Raises the NeuralNetwork Change event, simply propogates        /// original <see cref="NeuralNetwork">NeuralNetwork</see>         /// event up to the GUI        /// </summary>        /// <param name="nne">The NeuralNetworkEventArgs</param>        public virtual void On_NNChange(NeuralNetworkEventArgs nne)        {            if (NNChange != null)            {                // Invokes the delegates.                 NNChange(this, nne);            }        }        #endregion        #region Private Methods        /// <summary>        /// Evaluates a member of the population (of <see cref="NeuralNetwork">        /// NeuralNetworks</see>        /// </summary>        /// <param name="popMember">The member of the population to evaluate</param>        /// <returns>An overall error value for this population member, which is        /// the result of applying the complete training set to the population        /// member, with its current weight configuration</returns>        private double evaluate(int popMember)        {            double error = 0.0;            //loop through the entire training set            for (int i = 0; i <= train_set.GetUpperBound(0); i++)            {                //forward these new values through network                //forward weights through ANN                forwardWeights(popMember, getTrainSet(i));                double[] targetValues = getTargetValues(getTrainSet(i));                error += networks[popMember].getError(targetValues);            }            //if the Error term is < acceptableNNError value we have found            //a good configuration of weights for teh NeuralNetwork, so tell            //GA to stop looking            if (error < acceptableNNError)            {                bestConfiguration = popMember;                foundGoodANN = true;            }            //return error            return error;        }        /// <summary>        /// This event is simply here to propogate the underlying         /// <see cref="NeuralNetwork">NeuralNetworks</see> Change        /// event, to the gui. The gui has no visibility of the         /// array of <see cref="NeuralNetwork">NeuralNetworks</see>        /// so this trainer class propogates the events from the        /// <see cref="NeuralNetwork">NeuralNetworks</see> to the gui        /// </summary>        /// <param name="sender">The orginal <see cref="NeuralNetwork">NeuralNetwork</see>        /// that changed</param>        /// <param name="nne">The NeuralNetworkEventArgs</param>        private void GA_Trainer_NN_Change(object sender, NeuralNetworkEventArgs nne)        {            On_NNChange(nne);        }        /// <summary>        /// Returns the array within the 2D train_set array as the index        /// specfied by the idx input parameter        /// </summary>        /// <param name="idx">The index into the 2d array to get</param>        /// <returns>The array within the 2D train_set array as the index        /// specfied by the idx input parameter</returns>        private double[] getTrainSet(int idx)        {            //NOTE :            //            //If anyone can tell me how to return an array at index idx from            //a 2D array, which is holding arrays of arrays I would like that            //very much.            //I thought it would be            //double[] trainValues= (double[])train_set.GetValue(0);            //but this didn't work, so am doing it like this            double[] trainValues = { train_set[idx, 0], train_set[idx, 1] };            return trainValues;        }        /// <summary>        /// Forwards the weights from the input->hidden and also from        /// the hidden->output nodes, for the trainingSet        /// </summary>        /// <param name="popMember">The population member</param>        /// <param name="trainingSet">The training set to present to the         /// <see cref="NeuralNetwork"/>NeuralNetwork</param>        private void forwardWeights(int popMember, double[] trainingSet)        {            //forward weights through ANN            networks[popMember].pass_forward(trainingSet,getTargetValues(trainingSet));        }        /// <summary>        /// Returns a double which represents the output for the        /// current set of inputs.        /// In the cases where the summed inputs = 1, then target        /// should be 1.0, otherwise it should be 0.0.         /// This is only for the XOR problem, but this is a trainer        /// for the XOR problem, so this is fine.        /// </summary>        /// <param name="currSet">The current set of inputs</param>        /// <returns>A double which represents the output for the        /// current set of inputs</returns>        private double[] getTargetValues(double[] currSet)        {            //the current value of the training set            double valOfSet = 0;            double[] targs = new double[1];            for (int i = 0; i < currSet.Length; i++)            {                valOfSet += currSet[i];            }            //in the cases where the summed inputs = 1, then target            //should be 1.0, otherwise it should be 0.0            targs[0] = valOfSet == 1 ? 1.0 : 0.0;            return targs;        }        #endregion    }    #endregion    #region TrainerEventArgs CLASS    /// <summary>    /// Provides the event argumets for the     /// <see cref="GA_Trainer_XOR">trainer</see> class    /// </summary>    public class TrainerEventArgs : EventArgs    {        #region Instance Fields        //Instance fields        private int trainLoop = 0;        #endregion        #region Public Constructor        /// <summary>        /// Constructs a new TrainerEventArgs object using the parameters provided        /// </summary>        /// <param name="trainLoop">The current training loop</param>        public TrainerEventArgs(int trainLoop)        {            this.trainLoop = trainLoop;        }        #endregion        #region Public Methods/Properties        /// <summary>        /// gets the training loop number        /// </summary>        public int TrainingLoop        {            get { return trainLoop; }        }        #endregion    }    #endregion}

这是错误代码：

Assets/Scripts/System/NeuralNetwork/Trainer.cs(238,102): error CS0123: 方法或委托 `GA_ANN_XOR.GA_Trainer_XOR.GA_Trainer_NN_Change(object, NeuralNetwork.NeuralNetworkEventArgs)' 的参数与委托 `NeuralNetwork.Network.ChangeHandler(object, System.EventArgs)' 的参数不匹配

回答：

解决这个问题的一个简单方法是将以下代码：

public delegate void ChangeHandler (System.Object sender, EventArgs nne);

更改为：

public delegate void ChangeHandler (System.Object sender, NeuralNetworkEventArgs nne);

这样就可以解决这个问题了。