Bio-inspired Neural Network Research Articles

Real-time path planning for a robot to track a fast moving target based on improved Glasius bio-inspired neural networks

Path planning from the initial location to reach the target location has received considerable attentions. There are still some challenges in real-time path planning for tracking a fast moving target. In the application of tracking a fast moving target, the robot must move rapidly and correctly to follow the target. It requires an algorithm that enables real-time path planning in the fast changing environment. The Glasius Bio-inspired Neural Network (GBNN) model has been reported inefficient for the real-time path planning in the fast changing environments because its dynamic performance lags behind the fast environmental changes. This study puts forward several improvements for the GBNN model to improve its dynamic performance. An alternate weight function for GBNN model is also studied. It shows that all the proposed GBNN models can be parameter-fixed when applied in a specific case. Through theoretical analysis and comparative simulations, the improved models are stable and feasible in the real-time path planning in a rapidly changing environment.

Electronic nose using a bio-inspired neural network modeled on mammalian olfactory system for Chinese liquor classification.

The simplification of data processing is the frontier domain for electronic nose (e-nose) applications, whereas there are a lot of manual operations in a traditional processing procedure. To solve this problem, we propose a novel data processing method using the bio-inspired neural network modeled on the mammalian olfactory system. Through a neural coding scheme with multiple squared cosine receptive fields, continuous sensor data are simplified as the spike pattern in virtual receptor units. The biologically plausible olfactory bulb, which mimics the structure and function of main olfactory pathways, is designed to refine the olfactory information embedded in the encoded spikes. As a simplified presentation of cortical function, the bionic olfactory cortex is established to further analyze olfactory bulb's outputs and perform classification. The proposed method can automatically learn features without tedious steps such as denoising, feature extraction and reduction, which significantly simplifies the processing procedure for e-noses. To validate algorithm performance, comparison studies were performed for seven kinds of Chinese liquors using the proposed method and traditional data processing methods. The experimental results show that squared cosine receptive fields and the olfactory bulb model are crucial for improving classification performance, and the proposed method has higher classification rates than traditional methods when the sensor quantity and type are changed.

A Multi-Scale Map Method Based on Bioinspired Neural Network Algorithm for Robot Path Planning

With the wide application of Bioinspired Neural Network in the field of robot path planning, the environmental scale of robot path planning is getting larger, and the environmental resolution requirements are getting higher. However, with the increase of the environment size and resolution requirement, the neuronal activity value calculation cost and the time cost of the Bioinspired Neural Network will increase sharply. Aiming at this problem, this paper proposes an improved Bioinspired Neural Network path planning method based on Scaling Terrain. Using a Multi-Scale Map method and Dijkstra algorithm, the optimal path of a Coarse Scale Map is calculated. The optimal path obtained from the Coarse Scale Map is used to guide the neural network planning weights of the Fine Scale Map from the same terrain. Thus, the optimal path of the Fine Scale Map can be calculated by the improved BNN algorithm. Introducing this Multi-Scale Map Method into the Bioinspired Neural Network can greatly reduce the time cost of the Bioinspired Neural Network path planning algorithm and reduce the mathematical complexity. Simulation results in some computer integrated virtual environments further demonstrate the superiority of this method and the experimental results are encouraging.

Multi-AUV Cooperative Hunting Control with Improved Glasius Bio-inspired Neural Network

Cooperative hunting with multiple Autonomous Underwater Vehicles (AUVs) not only needs the AUVs to cooperate, but also demands real-time path planning to catch up with evading targets. In this paper a time-based alliance mechanism to form efficient dynamic hunting alliances is proposed. After that, during the active hunting stage, an improved neural network model based on a Glasius Bio-inspired Neural Network (GBNN) is presented for path planning to immediately achieve tracking of an intelligent target. This study shows that the improved GBNN model has good performance in real-time hunting path planning. From the simulation studies as described in this paper, both the hunting alliance formation mechanism and the proposed real-time hunting path planning strategy show their advantages. The results show that the improved GBNN model proposed in this paper can work well in the control of multiple AUVs to hunt for intelligent evading targets in environments containing obstacles.

KLN, a new biological koniocortex based unsupervised neural network: competitive results on credit scoring

Koniocortex-Like Network (KLN) model is a Bio-Inspired Neural Network structure that tries to replicate the architecture and properties of the biological koniocortex section of the brain. Based on its biological counterpart that behaves as a Winner-Take-All competitive system, this new structure is composed by different kind of artificial neurons that interplay naturally between them to create a model able to map autonomously the intrinsic knowledge included in a dataset into different classes. Biological properties of the human neural system as metaplasticity and intrinsic plasticity have been translated into this artificial model to create a self-organizing system applicable to multiple disciplines. This approach leads to a natural evolution of the network’s dynamics until obtaining the desired results. The KLN has been previously tested on several synthetic and real datasets, in this article we check its capability to deal with a different type of information by applying it to credit scoring problem, in particular to the classification of credit data from the Australian Credit Approval Database.

Stimulus space complexity determines the ratio of specialist and generalist neurons during pattern recognition

Neural processing layers built on divergent connectivity patterns display two types of stimulus-dependent responses: neurons that react to a few stimuli, specialists, and other ones that respond to a wide range of inputs, generalists. Specialists are essential for the discrimination of stimuli and generalists extract common and generic properties from them. This neural heterogeneity could have emerged because of animal adaptation to the environment. Thus, we suggest that there is a relationship between the percentage of specialists and generalists and the stimulus complexity. In order to study this possible relationship, we use patterns with different complexities in a bio-inspired neural network and calculate their classification errors for different ratios of these types of neurons. This study shows that, when the complexity of the stimuli is low, the minimum classification error is achieved with almost any specialist-generalist ratio. Thus, in this case, the role of these neurons during pattern recognition is unspecific. When this complexity is intermediate, both are needed to minimize the classification error, usually in a similar proportion. For increasing stimulus complexity, the importance of generalists decreases, until their relevance is fully nullified when the complexity is high. Therefore, if we adjust the specialist-generalist ratio to the complexity of patterns, we can build more effective neural networks for pattern recognition. Finally, we propose an estimation of stimulus complexity based on the proportion of these types of neurons observed by neural recordings. This offers the possibility to evaluate the stimulus complexity to which animals are adapted.

A Novel Cooperative Hunting Algorithm for Inhomogeneous Multiple Autonomous Underwater Vehicles

Cooperative hunting of multi-autonomous underwater vehicle (AUV) is an important research topic. Current studies concentrate on AUVs with the same speed abilities and mostly do not consider their speed differences. In fact, AUVs in a hunting group are often of different types and possess different maximum sailing speeds. For inhomogeneous multi-AUV, a novel time competition mechanism is proposed to construct an efficient dynamic hunting alliance. Hunting team with AUVs possessing higher speed abilities is more suitable for the vast underwater environment. In the pursuing stage, AUV needs to act fast enough to avoid the escape of evader. To achieve a quick and accurate pursuit, a combined path planning approach is presented, which combines a Glasius bio-inspired neural network model and a belief function. Simulation experiments demonstrate the feasibility and efficiency of the proposed algorithm in the cooperative hunting of inhomogeneous multi-AUV under dynamic underwater environment with intelligent evaders and multi-obstacle.

Multi-AUV cooperative target search and tracking in unknown underwater environment

For target search and tracking in unknown underwater environment, an integrated algorithm for a cooperative team of multiple autonomous underwater vehicles (Multi-AUV) is proposed by combining the Glasius bio-inspired neural network (GBNN) and bio-inspired cascaded tracking control approach to improve search efficiency and reduce tracking errors. Among them, the GBNN is mainly used to control a multi-AUV team in search of each targets. Once any target is found, the bio-inspired cascaded tracking control approach is applied to track it in case that it may escape. This integrated algorithm deals with various situations such as search for static or dynamic targets, and tracking of different trajectory in underwater environments with obstacles. The simulation results show that this integrated algorithm is of high efficiency and adaptability.

Task Assignment and Path Planning of a Multi-AUV System Based on a Glasius Bio-Inspired Self-Organising Map Algorithm

For multi-Autonomous Underwater Vehicle (multi-AUV) system task assignment and path planning, a novel Glasius Bio-inspired Self-Organising Map (GBSOM) neural networks algorithm is proposed to solve relevant problems in a Three-Dimensional (3D) grid map. Firstly, a 3D Glasius Bio-inspired Neural Network (GBNN) model is established to represent the 3D underwater working environment. Using this model, the strength of neural activity is calculated at each node within the GBNN. Secondly, a Self-Organising Map (SOM) neural network is used to assign the targets to a set of AUVs and determine the order of the AUVs to access the target point. Finally, according to the magnitude of the neuron activity in the GBNN, the next AUV target point can be autonomously planned when the task assignment is completed. By repeating the above three steps, access to all target points is completed. Simulation and comparison studies are presented to demonstrate that the proposed algorithm can overcome the speed jump problem of SOM algorithms and path planning in the 3D underwater environments with static or dynamic obstacles.

Sparse and burst spiking in artificial neural networks inspired by synaptic retrograde signaling

The bursting of action potential and sparse activity are ubiquitously observed in the brain. Although the functions of these activity modes remain to be understood, it is expected that they play a critical role in information processing. In addition, the functional role of retrograde signalling in neural systems is under intensive research. Therefore, we propose a bio-inspired neural network that is capable of demonstrating these activity modes as well as shifting themselves from normal to bursting or sparse modes by changing model parameter values. Accordingly, we model diffused retrograde signalling with different activity patterns in dendrites and presynaptic neurons. Using in a three-layered spiking neural network, simulation studies are conducted using different conditions and parameter values to find factors underlying the change in firing rate of output neurons. Our findings propose the application of retrograde signalling as a known synaptic mechanism for the development of artificial neural systems to encode environmental information by different spiking modes.

Multi-AUV Cooperative Target Search Algorithm in 3-D Underwater Workspace

To improve the efficiency of multiple Autonomous Underwater Vehicles (multi-AUV) cooperative target search in a Three-Dimensional (3D) underwater workspace, an integrated algorithm is proposed by combining a Self-Organising Map (SOM), neural network and Glasius Bioinspired Neural Network (GBNN). With this integrated algorithm, the 3D underwater workspace is first divided into subspaces dependent on the abilities of the AUV team members. After that, tasks are allocated to each subspace for an AUV by SOM. Finally, AUVs move to the assigned subspace in the shortest way and start their search task by GBNN. This integrated algorithm, by avoiding overlapping search paths and raising the coverage rate, can reduce energy consumption of the whole multi-AUV system. The simulation results show that the proposed algorithm is capable of guiding multi-AUV to achieve a multiple target search task with higher efficiency and adaptability compared with a more traditional bioinspired neural network algorithm.

Insect Bio-inspired Neural Network Provides New Evidence on How Simple Feature Detectors Can Enable Complex Visual Generalization and Stimulus Location Invariance in the Miniature Brain of Honeybees.

The ability to generalize over naturally occurring variation in cues indicating food or predation risk is highly useful for efficient decision-making in many animals. Honeybees have remarkable visual cognitive abilities, allowing them to classify visual patterns by common features despite having a relatively miniature brain. Here we ask the question whether generalization requires complex visual recognition or whether it can also be achieved with relatively simple neuronal mechanisms. We produced several simple models inspired by the known anatomical structures and neuronal responses within the bee brain and subsequently compared their ability to generalize achromatic patterns to the observed behavioural performance of honeybees on these cues. Neural networks with just eight large-field orientation-sensitive input neurons from the optic ganglia and a single layer of simple neuronal connectivity within the mushroom bodies (learning centres) show performances remarkably similar to a large proportion of the empirical results without requiring any form of learning, or fine-tuning of neuronal parameters to replicate these results. Indeed, a model simply combining sensory input from both eyes onto single mushroom body neurons returned correct discriminations even with partial occlusion of the patterns and an impressive invariance to the location of the test patterns on the eyes. This model also replicated surprising failures of bees to discriminate certain seemingly highly different patterns, providing novel and useful insights into the inner workings facilitating and limiting the utilisation of visual cues in honeybees. Our results reveal that reliable generalization of visual information can be achieved through simple neuronal circuitry that is biologically plausible and can easily be accommodated in a tiny insect brain.

Tracking control of nonaffine systems using bio-inspired networks with auto-tuning activation functions and self-growing neurons

This paper presents a bio-inspired artificial neural network (Bio-ANN) to tackle the tracking control of complex dynamic systems. The proposed Bio-ANN is motivated by the operant conditioning of biological systems, in which we not only adaptively tune the weights but also adjust the structural parameter of basis functions automatically, significantly enhancing the learning capability of the proposed control. Furthermore, the size of the dataset needed for online ANN training is small and the overall computational cost is low. With the help of such Bio-ANN, we develop a control scheme for a class of single-input single-output non-affine systems, where the operant conditioning bionic model (OCBM) is utilized. By comparing the proposed method with existing self-organizing approaches via numerical simulations, we verify that a faster convergent rate is achieved with better control precision by using the proposed OCBM based control approach.

A Dynamic Bioinspired Neural Network Based Real-Time Path Planning Method for Autonomous Underwater Vehicles.

Real-time path planning for autonomous underwater vehicle (AUV) is a very difficult and challenging task. Bioinspired neural network (BINN) has been used to deal with this problem for its many distinct advantages: that is, no learning process is needed and realization is also easy. However, there are some shortcomings when BINN is applied to AUV path planning in a three-dimensional (3D) unknown environment, including complex computing problem when the environment is very large and repeated path problem when the size of obstacles is bigger than the detection range of sensors. To deal with these problems, an improved dynamic BINN is proposed in this paper. In this proposed method, the AUV is regarded as the core of the BINN and the size of the BINN is based on the detection range of sensors. Then the BINN will move with the AUV and the computing could be reduced. A virtual target is proposed in the path planning method to ensure that the AUV can move to the real target effectively and avoid big-size obstacles automatically. Furthermore, a target attractor concept is introduced to improve the computing efficiency of neural activities. Finally, some experiments are conducted under various 3D underwater environments. The experimental results show that the proposed BINN based method can deal with the real-time path planning problem for AUV efficiently.

A Bioinspired Neural Network for Data Processing in an Electronic Nose

A novel bioinspired neural network is proposed as a replacement for traditional data processing methods in the electronic nose (e-nose) that we designed. This neural network mimics the main structures of the mammalian olfactory system. It contains olfactory sensing neurons, mitral cells, and granule cells. The proposed method can directly use the raw data collected from sensors in the e-nose without any signal preprocessing, feature selection, or reduction. The output neurons of the network can change the sensors’ responses into two new time series, of which we only use the variances to perform classification. This significantly simplifies the data processing procedure in e-noses. In order to test the performance of the proposed bioinspired neural network in the e-nose, two sampling methods, three classification methods, and seven kinds of Chinese liquors were employed. The highest classification rates of the proposed method and traditional method are 100% and 93%, respectively.

A multirobot platform based on autonomous surface and underwater vehicles with bio-inspired neurocontrollers for long-term oil spills monitoring

This paper describes the BUSCAMOS-Oil monitoring system, which is a robotic platform consisting of an autonomous surface vessel combined with an underwater vehicle. The system has been designed for the long-term monitoring of oil spills, including the search for the spill, and transmitting information on its location, extent, direction and speed. Both vehicles are controlled by two different types of bio-inspired neural networks: a Self-Organization Direction Mapping Network for trajectory generation and a Neural Network for Avoidance Behaviour for avoiding obstacles. The systems' resilient capabilities are provided by bio-inspired algorithms implemented in a modular software architecture and controlled by redundant devices to give the necessary robustness to operate in the difficult conditions typically found in long-term oil-spill operations. The efficacy of the vehicles' adaptive navigation system and long-term mission capabilities are shown in the experimental results.

Bio-inspired neural network with application to license plate recognition: hysteretic ELM approach

Purpose This paper aims to present a bio-inspired neural network for improvement of information processing capability of the existing artificial neural networks. Design/methodology/approach In the network, the authors introduce a property often found in biological neural system – hysteresis – as the neuron activation function and a bionic algorithm – extreme learning machine (ELM) – as the learning scheme. The authors give the gradient descent procedure to optimize parameters of the hysteretic function and develop an algorithm to online select ELM parameters, including number of the hidden-layer nodes and hidden-layer parameters. The algorithm combines the idea of the cross validation and random assignment in original ELM. Finally, the authors demonstrate the advantages of the hysteretic ELM neural network by applying it to automatic license plate recognition. Findings Experiments on automatic license plate recognition show that the bio-inspired learning system has better classification accuracy and generalization capability with consideration to efficiency. Originality/value Comparing with the conventional sigmoid function, hysteresis as the activation function enables has two advantages: the neuron’s output not only depends on its input but also on derivative information, which provides the neuron with memory; the hysteretic function can switch between the two segments, thus avoiding the neuron falling into local minima and having a quicker learning rate. The improved ELM algorithm in some extent makes up for declining performance because of original ELM’s complete randomness with the cost of a litter slower than before.

A multi-AUV cooperative hunting method in 3-D underwater environment with obstacle

A multi-AUV cooperative hunting algorithm based on bio-inspired neural network is proposed for 3-D underwater environment with obstacle. Firstly, AUV 3-D working environment is represented by biological inspired neural network model, and there is one-to-one correspondence between each neuron in neural network and the position of the grid map of underwater environment. Then the activity value of neurons is used to guide each hunting AUV navigation and obstacle avoidance. A high efficient path for each hunting AUV is arranged and finally the target is surrounded by AUVs. Lastly, to demonstrate the effectiveness of the proposed algorithm, simulation results are given in this paper.

ISI Free Channel Equalization for Future 5G Mobile Terminal Using Bio-inspired Neural Networks

This article presents the concept of future 5th generation (5G) wireless communications as an existing beyond 4G systems like long term evolution (LTE) which means the indicate of 5G scenarios can be introduced in near future. Therefore, this paper deals of a future 5G mobile terminal for applications to uplink transmission in a multiuser LTE scheme. Unfortunately, LTE-uplink inherently generates significant inter-symbol interference especially high bandwidth scenarios. The result is a rise to mutual interference among active users with an increased error rate. This incidence eventually causes non-orthogonal user spreading codes. Moreover, this drawback is known as the multiple access interference episodes which demonstrate high computational complexity and enhances symbol error rate at the receiving end and degrades the communication quality. Most of the related work has been claimed iterative linear minimum mean square error (LMMSE) detection requires a matrix inversion role which has a high computational complexity and contains a combinatorial optimization problem. Consequently, the LMMSE does not meet the requirement to implement real-time detection with low complexity and thus limiting its application. Therefore, we propose an acceptable bio-inspired neural network (NN) in the case of single and multilayer NNs with supervised learning particularly Levenberg---Marquardt backpropagation learning algorithm to improve the convergence speed. Simulation results performed with highest approaches highlights a better act for the proposed system.

Multi-AUV Hunting Algorithm Based on Bio-inspired Neural Network in Unknown Environments

The multi-AUV hunting problem is one of the key issues in multi-robot system research. In order to hunt the target efficiently a new hunting algorithm based on a bio-inspired neural network has been proposed in this paper. Firstly, the AUV's working environment can be represented, based on the biological-inspired neural network model. There is one-to-one correspondence between each neuron in the neural network and the position of the grid map in the underwater environment. The activity values of biological neurons then guide the AUV's sailing path and finally the target is surrounded by AUVs. In addition, a method called negotiation is used to solve the AUV's allocation of hunting points. The simulation results show that the algorithm used in the paper can provide rapid and highly efficient path planning in the unknown environment with obstacles and non-obstacles.