Update README.md

README.md

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 # BP_system
+// Step 3: Sigmoid Function
+double sigmoid(double x) {
+    return 1 / (1 + exp(-x));
+}
+
+// Step 4: Initialize Hidden Layer Nodes
+void init() {
+    // Assuming you have predefined constants for the number of nodes and other parameters
+    const int num_hidden_nodes = 10; // Adjust according to your network architecture
+
+    // Initialize hidden layer nodes
+    for (int i = 0; i < num_hidden_nodes; i++) {
+        // Allocate memory for weights and weight corrections
+        double* weights = (double*)malloc(sizeof(double) * num_input_nodes); // Assuming num_input_nodes is defined
+        double* weight_corrections = (double*)malloc(sizeof(double) * num_input_nodes);
+
+        // Initialize weights with random values
+        for (int j = 0; j < num_input_nodes; j++) {
+            weights[j] = (double)rand() / RAND_MAX * 2.0 - 1.0;
+            weight_corrections[j] = 0.0;
+        }
+
+        // Initialize bias and bias correction
+        double bias = (double)rand() / RAND_MAX * 2.0 - 1.0;
+        double bias_correction = 0.0;
+
+        // Store these values in your data structure for the hidden layer
+        // This is where you need to have a data structure to hold your network parameters
+    }
+}
+
+// Step 5: Forward Propagation from Hidden Layer to Output Layer
+void forward_propagation() {
+    // Assuming you have predefined constants for the number of nodes and other parameters
+    const int num_output_nodes = 5; // Adjust according to your network architecture
+
+    // Assuming you have initialized your hidden layer and stored it in some data structure
+
+    // Loop through each output node
+    for (int i = 0; i < num_output_nodes; i++) {
+        // Initialize sum
+        double sum = 0.0;
+
+        // Loop through each hidden layer node
+        for (int j = 0; j < num_hidden_nodes; j++) {
+            // Calculate the weighted sum of inputs
+            sum += hidden_layer[j].value * hidden_layer[j].weights[i];
+        }
+
+        // Subtract the bias
+        sum -= output_bias[i];
+
+        // Apply sigmoid activation
+        output_layer[i].value = sigmoid(sum);
+    }
+}
+
+// Step 6: Main Function
+int main() {
+    // Initialize your neural network
+    init();
+
+    // Perform forward propagation
+    forward_propagation();
+
+    // Rest of your code...
+    return 0;
+}