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import numpy as np
from scipy.integrate import solve_ivp
import matplotlib.pyplot as plt
class SelfAwareNetwork:
def __init__(self, num_neurons, learning_rate):
self.num_neurons = num_neurons
self.learning_rate = learning_rate
self.weights = np.random.rand(num_neurons)
self.state = np.zeros(num_neurons)
def activation_function(self, x, t, n):
hbar = 1.0545718e-34 # Reduced Planck constant in J·s
omega = 1/np.sqrt(137) # Angular frequency related to fine-structure constant
term1 = hbar * omega * (n + 0.5)
term2 = np.sin(omega * x + np.pi/4) * np.exp(-t)
return term1 + term2
def neuron_dynamics(self, t, y):
n = np.arange(self.num_neurons)
dydt = -y + self.activation_function(y, t, n)
return dydt
def update_weights(self, state):
self.weights += self.learning_rate * state
def solve_dynamics(self, t_span, y0):
sol = solve_ivp(self.neuron_dynamics, t_span, y0, method='RK45', vectorized=False)
return sol.t, sol.y
def evaluate_performance(self, target_state):
error = np.linalg.norm(self.state - target_state)
return error
def adjust_learning_rate(self, performance_metric):
if performance_metric > 0.1:
self.learning_rate *= 0.9
else:
self.learning_rate *= 1.1
def self_optimize(self, target_state, t_span, y0):
t, y = self.solve_dynamics(t_span, y0)
self.state = y[:, -1]
performance = self.evaluate_performance(target_state)
self.adjust_learning_rate(performance)
self.update_weights(self.state)
def plot_state_evolution(self, t, y):
plt.plot(t, y.T)
plt.xlabel('Time')
plt.ylabel('Neuron States')
plt.title('State Evolution of Neurons')
plt.show()
# Example usage
network = SelfAwareNetwork(num_neurons=3, learning_rate=0.01) # Reduced the number of neurons
t_span = (0, 5) # Shortened the time span
y0 = np.random.rand(3) # Adjusted for the reduced number of neurons
target_state = np.ones(3)
network.self_optimize(target_state, t_span, y0)
t, y = network.solve_dynamics(t_span, y0)
network.plot_state_evolution(t, y)
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