cite this article,

Efficient removal of antidepressant Flupentixol using graphene oxide/cellulose nanogel composite: Particle swarm algorithm based artificial neural network modelling and optimization K. Balasubramani, N. Sivarajasekar, S. Muthusaravanan, K. Ram, Mu. Naushad, Tansir Ahamad, Gaurav Sharma PII: S0167-7322(20)35406-4 DOI: https://doi.org/10.1016/j.molliq.2020.114371 Journal of Molecular Liquids

Swarm based neural network for predicting adsorption percentage or adsorption capacity from process parameters.
Initially, the PSO algorithm-based ANN reads input and target data from the user to create a feed forward neural network of size ‘n’. The following steps were implemented to train the developed feed forward ANN using PSO.

i) Initialization of particle population in PSO with position and velocity

ii) Evaluation of initial fitness of each particle to find personal experience (Pbest) and overall experience (Gbest)

iii) Setting the iteration count K=1

iv) Update the position and velocity of each particle in PSO

v) Evaluate the fitness of each particle and update Gbest and Pbest

vi) Check the K value if K <= Maxite then K = k+1 and if not print the optimal weights and bias to ANN.