Enterprise Information Security Management Using Internet of Things Combined with Artificial Intelligence Technology

Algorithm 1

Internet of Things-Based Enterprise Modernization Management BP-PSO Algorithm

	clear;
	clc;
	p = 0 : 0.01: 2; % sample input
	t = sin(pi ∗ p); % sample output
	n = 3; % the number of neurons in the hidden layer
	net = newff(p,t, n, {'tansig','purelin'}, 'trainlm'); swarmCount = 20; % particle count.
	swarmLength = 10; % particle length
	vMax = 20; % maximum speed of particle movement
	pMax = 2; % maximum position of particle motion
	swarm = rand (swarmCount, swarmLength); % initial particle swarm, that is, the position of the particle
	= rand (swarmCount, swarmLength); % particle speed.
	swarmfitness = zeros (swarmCount, 1, 'double'); % particle fitness value.
	pBest = rand (swarmCount, swarmLength); % individual optimal value.
	pBestfitness = zeros (swarmCount, 1, 'double'); % individual optimal fitness valuepBestfitness(:,) = 100;
	gBest = rand (1, swarmLength); % global optimum.
	gBestfitness = 100; % global optimal fitness value
	c1 = 2;
	c2 = 2;
	wmax = 0.95;
	wmin = 0.25;
	maxEpoch = 2000; % maximum training times
	errGoal = 0.01; % expected error minimum
	epoch = 1;
	while (epoch < maxEpoch && gBestfitness > errGoal)
	for i = 1: swarmCount
	% calculating the fitness value of the particle
	net.iw{1, 1} = swarm(i, 1 : 3)';
	net.b{1} = swarm(i, 4 : 6)';
	net.lw{2,1} = swarm(i, 7 : 9);
	net.b{2} = swarm(i, 10 : 10);
	tout = sim(net, p); % output of prediction.
	sse = sum((tout - t).^ (2)/length(t);
	swarmfitness(i, 1) = sse;
	% updating the individual optimal value
	if (pBestfitness(i, 1) > sse)
	pBestfitness(i, 1) = sse;
	pBest(i,) = swarm(i,);
	% updating the global optimum
	if(gBestfitness > sse)
	gBestfitness = sse;
	gBest(1,) = swarm(i,);
	end
	end
	end
	% updating particle velocity and position
	W(epoch) = wmax-((wmax-wmin)/maxEpoch)∗epoch;
	for i = 1: swarmCount
	(i,) = W(1)∗v(i,) + c1 ∗ rand(1, 1) ∗ (pBest(i,) - swarm(i,)) + c2 ∗ rand(1, 1) ∗ (gBest(1,) - swarm(i,)); % velocity update
	tmp = (i,) > vMax;
	(i, tmp) = vMax; % particles that exceed the bounds are converted to bounds
	swarm(i,) = swarm(i,) + (i,); % particle position update
	tmp = find(swarm(i,) > pMax); % particles that exceed the bounds are converted to bounds
	swarm(i, tmp) = pMax;
	end
	epoch = epoch + 1;
	end
	net.iw{1, 1} = gBest(1, 1 : 3)';
	net.b{1} = gBest(1, 4 : 6)';
	net.lw{2, 1} = gBest(1, 7 : 9);
	net.b{2} = gBest(1, 10 : 10);
	tout = sim(net, p);
	figure(1)
	plot(p, t, 'k-');
	hold on;
	plot(p, tout, 'b-');